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Asian Spine J > Volume 14(4); 2020 > Article
Azimi, Yazdanian, Benzel, Aghaei, Azhari, Sadeghi, and Montazeri: A Review on the Use of Artificial Intelligence in Spinal Diseases

Abstract

Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

Introduction

Artificial neural network (ANN) models represent a mathematical rendition of the human nervous system that have been broadly applied to solve various nonlinear problems in the biomedical arena [1,2]. ANN is a machine-learning technique adept at learning the relationships between specified input and output variables. Neural networks have been used predominantly for pattern-recognition regarding prediction and classification. The history and theory of ANNs has been reported in detail elsewhere [1-4]. In addition, the advantages and disadvantages of ANN have also been previously reported by us [1,2]. ANN is a promising field with numerous applications across various branches of medicine wherein it serves as a decision support tool to provide economic solutions to time and resource management [5]. Recently, artificial intelligence and related algorithms have facilitated rapid advances in the assessment of spinal diseases [2,5]. Moreover, ANNs are applied for clinical diagnosis, prognosis, outcome prediction following spinal surgery, research, and biomechanical assessments of spinal diseases [2]. However, there has been little utilization of ANNs in spine clinical practice. Given the recent advances in the management of spinal diseases and the fundamental role of decision, this comprehensive review is conducted aiming to describe the ANN-aided decision support system for management of spinal diseases, including diagnosis, prognosis, and outcome prediction.

Methods

ANN based methodology has been reported in detail elsewhere [1,2].

1. Search strategy

A detailed search of original articles was performed on Medline (through the PubMed search engine) and Scopus (Elsevier) databases to identify the applications of ANNs diagnosis, prediction, and prognosis of spinal disease. The review is intended to include all the full-text publications in the English biomedical journals. The following combinations of keywords were searched within the titles and abstracts: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The structural keywords were selected due to their likelihood of being mentioned in either the title or the abstract of relevant articles. Since the first study of ANNs in spine diseases published in 1993, we performed a comprehensive search covering the period 1993 to 2020. An initial search was carried out in November 2019 and updated thrice in 2020 (January, February, and March).

2. Inclusion and exclusion criteria

All research articles on ANNs in spinal diseases were screened in the Scopus and Medline databases. Each article was independently reviewed by two reviewers and disagreements were sent to each other for resolution, only the articles emphasize on the most recent advances and their application in the spinal diseases were included. Publications on other disease conditions or animal studies were excluded.

3. Data synthesis

The findings from the all identified studies were summarized in a descriptive table, including authors’ names, publication year, study setting, study sample, disease conditions (if relevant data is available), and main results or conclusions. Subsequently, the findings were sorted chronologically.

Results

1. Statistics

The reviewers identified and screened 3,653 unique abstracts. After screening, 3,564 papers were found to be irrelevant. Then, the remaining 89 papers were examined and the full text were reviewed for eligibility criteria. Ultimately, we included 79 studies on qualitative analysis. The flowchart of the literature review process is illustrated in Fig. 1. Overall, we pursued four categories of studies, namely diagnosis, progression, outcome prediction, and use of ANNs in the biomechanical assessments of spinal diseases. The main findings were grouped and presented as follows [6-81].

2. Diagnosis

In spinal diseases, ANNs have been successfully tested for diagnosis of pediatric low back pain [6,9,11], normal and abnormal cervical spine vertebra [8], scoliosis spinal deformity [7,10], and identification of risk factors associated with the development of complications following posterior lumbar spine fusion [23]. Besides, artificial intelligence models have been employed for medical image analysis assessment, such as those portrayed in the Table 1.

3. Spinal prognosis

In addition to the application of in identifying the patients with high risk of hypotension during spinal anesthesia [52], ANNS have been tested to determine the prognosis of low back pain [51,53] aiming to automatically predict (and identify risk factors for) the complications following posterior lumbar spine fusion surgery [16], and to develop and evaluate a set of predictive models for common adverse events after spine surgery [57]. Also, ANNs are useful for developing novel computational tools to predict clinical outcomes, return to work, physical disability, occurrence of complications, readmission rates, walking ability, discharge, and disposition following spine surgery [54,55,58]. Neural network techniques have also been applied to develop predictive algorithms for postoperative complications following anterior cervical discectomy and fusion [56], and to evaluate clinically relevant improvement in leg pain, back pain and functional disability after lumbar disc herniation (LDH) surgery [59], and to automatically quantify muscle fat infiltration following whiplash injury [62]. In addition, ANNs have been shown to accurately predict survival, discharge and hospital readmission rates following spinal metastasis surgery [57,60,61], to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion [63], and to predict prolonged opioid prescription after surgery for LDH [64]. Last but not least, ANNs have been used to predict the survival rate following a spinopelvic chondrosarcoma diagnosis [65] and to predict the occurrence of four types of major complications, namely cardiac complications, wound complications, venous thromboembolism, and mortality in the patients undergoing spine fusion, and it has achieved better results than the commonly used clinical scoring methods [16]. A summary of the studies is shown in Table 2.

4. Outcome prediction

Table 3 summarizes the studies that used ANNs for outcome prediction. ANNs have been used to predict outcome in lumbar spinal stenosis (LSS) [3] and LDH [4], predict recurrent LDH [66], enhance surgical decision making for LSS [67], develop ANN algorithms for prediction of in-hospital and 90-day post-discharge mortality in spinal epidural abscess [68], predict non-routine discharge for patients undergoing surgery for lumbar disc disorders [69], assess vertebral strength and predict vertebral fracture risk in elderly patients [70], predict 30- day readmission after posterior lumbar fusion [71], and predict surgical site infections after posterior spinal fusion [72].

5. The use of artificial neural networks for the biomechanical assessments of spinal diseases

A clear understanding of biomechanical principles is important in the management of spinal disorders. There are ANNs studies focused on the biomechanics of spine via clarification of joint moments, spinal loads, and muscle forces [75-77]. Other than that, application of ANNs for optimization of the design of spinal pedicle screws [78], prediction of vertebral strength through machine learning models [70], determination of the consistency of the patient pain drawing in lumbar spine disease [73], prediction of low bone mineral density [74], recognition of low back pain patients from healthy population performed static standing tasks [79], estimation of three-dimensional whole-body posture, lumbosacral moments and spinal loads during load-handling activities [80] and automated tracking of lumbar vertebras with rotated bounding boxes in digitalized video fluoroscopic imaging, and motion and gait analysis [81] have been reported. These findings are summarized in Table 4.

Discussion

To the best of our knowledge, this is the first review devoted exclusively to an application of ANN in support of decision for management of spinal disease. Our findings offer a summary of relevant publications and a roadmap to guide future research related to the use of ANNs in spinal disease. Precisely, our findings showed that ANNs are powerful tools with the ability to improve understanding of predictive metrics, prognosis, diagnosis and biomechanical assessment in spinal diseases. Moreover, ANNs have shown consistent superiority over the traditional statistical approaches. In light of the continuous development of hardware and software methods, and advanced computational science and technology, wider consideration and broader application artificial intelligence in spinal disease is expected in the near future [2].
The number of publications on neural networks in spinal diseases has increased rapidly over the past few years, wherein a majority of the publications were in the domain of diagnosis of spinal disorders, followed by prognosis, prediction, and biomechanical for spinal applications. A number of ANN studies have focused on preoperative assessment, planning, intraoperative assistance and outcome prediction in spine surgery. Recently, Khor et al. [82] successfully developed a state-of-the-art use of a logistic regression (LR) model to predict the patient-reported outcomes in lumbar fusion surgery. They developed a clinical prediction tool model to determine the probabilities of improvement in function, back pain, and leg pain in lumbar fusion candidates at 1-year follow-up after surgery. This model showed a good accuracy in the validation cohorts. The same group also provided an online version of their prediction model for public use (https://becertain.shinyapps.io/lumbar_fusion_calculator/), where a clinician and/or patient can enter the individual demographics to predict a patient’s likelihood of benefiting from a lumbar fusion procedure [83]. Besides, Karhade et al. [64] developed a machine learning tool for predicting prolonged postoperative opioid prescription in the patients undergoing LDH surgery (https://sorg-apps.shinyapps.io/lumbardiscopioid/). It is worth mentioned that preoperative prediction of opioid use could improve the risk stratification, shared decision-making, and patient counseling before LDH surgery [84]. In addition, Karhade et al. [61] developed a machine learning tool to automatically predict 90-day and 1-year mortality in spinal metastatic disease (https://sorg-apps.shinyapps.io/spinemetssurvival/) [85]. Meanwhile, the same group also developed a machine learning algorithm for predicting discharge disposition after elective inpatient surgery for lumbar disc disease [55], the model is available at https://sorg-apps.shinyapps.io/discdisposition/ [86]. Furthermore, there is also an application at https://sorg-apps.shinyapps.io/spinemets/ [87] which allows prediction of 30-day mortality after surgery for spinal metastatic disease [60]. Nonetheless, these machine learning tools are fitted for general educa - tional purposes and they are not capable of substituting the professional medical advice, consultation, diagnosis, or treatment [82-87]. One might inquire about how the ANNs can assist in the clinical decision-making process? Spine neural network tools will never replace human experts, but it helps in screening and can be used by the experts to validate their diagnosis, prognosis, and predic - tion. More importantly, ANNs can be used to identify the variables that experts may not observe, thus enhancing the diagnostic acumen of experts. As aforementioned, the currently available web applications are not a good fit with the clinical practice setting. However, development of this software tool is a prerequisite for an international consul - tancy group to satisfy the diverse needs as randomized clinical trials (RCT) data exists that specifically examines this tool. Nevertheless, ANNs will never replace human expert decision-making, but it can assist in validating the routine decision-making process [2].
Few neural network studies have focused on medi - cal imaging analysis. There are a wide variety of medical imaging modalities and magnetic resonance imaging (MRI) is majorly applied for clinical diagnosis and prog - nosis. Recently, some studies have demonstrated suc - cessful application of artificial intelligence algorithms for spine medical image segmentation [17,19,20,22,24,27,3 3,35,38,47,49], computer-aided spine diagnosis [84-87], and disease detection and classification [10,45]. In other words, spinal images could be analyzed, processed, and categorized by using neural network. By selecting a suit - able training set and learning process, neural networks is appropriate for recognition of unusual images [88].Artifi - cial intelligence will play a vital role in the development of medical image analysis methods. However, deep learning architecture requires a large amount of training data and computational power. Currently, there is also a rising in - terest with respect to the digital image analysis solutions with artificial intelligence for clinical applications. Such applications aim to increase diagnostic and prognostic ac - curacy, reliability, and efficiency by enabling quantitative image analysis. For instance, Oxford SpineNet software system, a machine learning based system for automated analysis of spinal T2 MRI scans acquired from a DICOM (Digital Imaging and Communications in Medicine) file, introduced by Jamaludin et al. [14,89] helps in clinical and algorithmic research [90]. This system can extract a wide range of relevant measurements from magnetic resonance (MR) images automatically including Pffirrmann grades, Modic changes, spinal stenosis, grading LSS into four grades, and disc herniation. The software needs to be able to learn without human input and classify multiple radiological features simultaneously. Therefore, the SpineNet software system adopts a neural network which allows it to learn and classify multiple scores at the same time. The SpineNet software tool is available online at http://zeus.robots.ox.ac.uk/spinenet/. Meanwhile, the authors stated that this is neither a diagnostics tool nor a medical device; hence, it should be used for research only [90]. In the future, ANN tools for the spinal workflow may help to empower the spine surgeon in management of spinal diseases, positively engage the patients, reduce the probability of error and improve spine surgeon efficiency.
ANNs have been effectively applied for biomechanics of spine, such as estimation of loads and stresses [80], estimation of the material properties of biological tissues [74], and analysis of the motion and gait [81]. A clear understanding of biomechanical principles is vital for the treatment of spinal disorders [91]. In view of the direct and invasive in vivo measurements of spinal loads and muscle forces, researchers have decided to apply ANNs for spinal biomechanical assessment by using different possible models [77]. Yet, the use of artificial intelligence techniques in clinical biomechanics of the spine is still in its infancy. However, continued assessment using ANN methods and understanding of mathematical and bioengineering principles will ensure the discovery of new solutions and more effective ways of helping spine patients, especially spine surgery [75-78]. In general, at the present time, there is no clinical application available for biomechanics of spine. Nonetheless, the aforementioned studies clearly demonstrate the potential of neural network in this arena.
There was considerable heterogeneity in the modeling methods used. Studies varied with regard to the inclusion criteria, input and output variables used, machine learning techniques applied, and models performance for the evaluation of the four categories of issues discussed above. Yet, LR, neural networks, and support vector machines (SVM) are the most commonly applied computer-based algorithms. However, these algorithms are developing and require further improvement. As an example, the following machine learning techniques are the most recommended to be employed: Bi-directional Long Short-Term Memory, Deep Learning and Neural Network, Extreme Machine Learning, SVM and TBasts, Random Forest, and Boosting [92]. On the other hand, the comparison of different neural network algorithms could assist in the selection of the best model for making sure that it has the best performance. However, it is also crucial to consider the advantages and disadvantages of these algorithms based on the dataset details and planed application to make the best choice [92]. Hence, there is a need for method standardization in order to apply ANNs spine clinical practice.Although the current research highlights the promise and potential of neural networks in spine disorders, there is continuing debate whether neural networks could or should be used in routine clinical practice. At present, there is a paucity of literature on the clinical application of neural networks in spine clinical decision-making. In addition, there are no specific consensus recommendations for designing an optimal ANN for clinical practice [2]. Hence, in order to develop an ANN tool for spine clinical practice, it is recommended that an international center need to consider the following subjects: (1) to create a multidisciplinary team of spine clinicians, engineers and data scientists to evaluate ANN tools; (2) to design methods of data collection for training and testing, and standardization for each spinal disorders; (3) to select the best neural network algorithm; (4) to develop and improve user-friendly software environment; (5) to validate (“model testing”); (6) to assess ANNs as tools for performing RCT; and (7) to provide up-to-date ANN models which could handle higher patient data (big data) entry for augmenting augment decision-making efficacy [2]. In general, the preliminary results from tools are satisfactory. However, it still requires many efforts to make the ANN tools more accurate and to make the idea practically achievable.
ANN technology has been attracting substantial attention in spinal disease, but there are challenges to be implemented in clinical setting. Several limitations exist in most of the studies on ANNs in spinal diseases, including largely heterogeneous study design, data analysis, modeling technique, training and testing features applied, algorithms employed, and end points. Hence, a focused synthesis of the literature cannot be provided. In addition, the search strategy was limited to the keywords in the titles or abstracts of publications. Thus, we might have missed some papers. Secondly, this work restricted the query search for articles in PubMed. Thirdly, non-English publications were not considered in this study. We believe that the research regarding the application of ANNs in spinal diseases have also been published in other languages. Fourthly, most ANNs algorithms in these studies were validated with their dataset, it may be lack of external validation and generalizability of their results. Fifthly, some studies did not compare ANNs with conventional statistical analysis; hence, a comparison between any two models is limited [2].

Conclusions

The evidence suggests that ANNs can be successfully used for spinal disease to manage its diagnosis, prognosis and outcome prediction. Further ANNs algorithm retraining, generalizability of models, data standardization in neural networks, and focus on the application of ANNs as a tool in clinical spine practice, will augment decision-making efficacy.

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Acknowledgments

The authors thank the staff of the Neurosurgery Unit at Imam-Hossain Hospital, Tehran, Iran.

Fig. 1.
Flowchart of literature search, selection, and identification.
asj-2020-0147f1.jpg
Table 1.
A list of papers on ANN used in spine diagnosis
Author Year Country No. of sample size
Model type Conditions Comparison with non-ANN models Main focus Results/conclusion(s)
Training Testing
Bishop et al. [6] 1997 USA 161 22 MLP: resilient propagation neural networks, and radial basis function neural networks LBP Yes To determine specific characteristics of trunk motion associated with different categories of spinal disorders and to determine whether an ANNs can be effective in distinguishing patterns. The neural network classifier produced the best results with up to 85% accuracy on “validation” data.
Jaremko et al. [7] 2001 Canada 49 18 MLP: a three-layer back-propagation artificial neural network using the Levenberg-Marquardt algorithm Spinal deformity NR To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an ANNs. The ANNs estimated the maximal Cobb angle within 6° in 63% of the test data. set and was able to distinguish a Cobb angle greater than 30° with a sensitivity of 1.0 and specificity of 0.75. ANNs of full-torso scan imaging showed promise to accurately estimate scoliotic spinal deformity in a variety of patients.
Stanley et al. [8] 2001 USA 118 118 MLP Cervical spine vertebra Yes Comparing various classifiers including an ANNs, K-Means algorithm, quadratic discriminant classifier and LVQ3. Results from those classifiers are reported for recognizing vertebrae as normal or abnormal.
Liszka-Hackzell et al. [9] 2002 Sweden 30 10 MLP LBP NR To explore new techniques of patient assessment that may prospectively identify of patients experience extended chronic pain and disability at risk of developing poor outcomes. There was a good correlation between the true and predicted values for general health (r=0.96, p<0.01) and mental health (r=0.80, p<0.01). ANNs can be applied effectively to categorizing patients with acute and chronic LBP.
Lin et al. [10] 2008 USA 25 Patterns 12 Patterns MLP: a multilayer feedforward, back-propagation ANN Spinal deformity NR To identify the classification of unspecified patterns of the scoliosis spine models The accuracy was within 2.0 mm. The study showed that the data do not seem to be adequate enough due to participate study were small. Nevertheless, ANNs was useful with high accuracy to identify the classification patterns of the scoliosis spinal deformity.
Sari et al. [11] 2010 Türkiy 169 169 MLP: the designed ANN consisted of feed-forward back propagation, two hidden layers LBP NR Comparison of ANNs and adaptive neuro-fuzzy inference system for the assessment of the LBP The results showed that the ANNs and adaptive neuro-fuzzy inference system behave very similar to real data. The use of these systems can be used to successfully diagnose the back pain intensity.
Veronezi et al. [12] 2015 Brazil 68 Radiographies for the training stage 68 Images for tests and 70 for validation Neural networks Osteoarthritis of the lumbar spine NR For the diagnosis of osteoarthritis of the lumbar spine The validation was carried out on the best results, achieved accuracy of 62.85%, sensitivity of 65.71%, and specificity of 60%. Although the neural network presented an average efficacy, because this was an innovative study, its results showed a potential for the use of computer-based artificial neural networks to assist and support practitioners.
Zhang et al. [13] 2017 China 235 Radiographs 105 Radiographs DNN Scoliosis assessment Yes To perform automatic measurements of Cobb angle for scoliosis assessment The differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5∘. The variability of Cobb angle measurements could be reduced if the DNN system was trained with enough vertebral patches.
Jamaludin et al. [14] 2017 UK 90% in a training set of 1,806 patients 10% in an independent sample of 203 patients CNN Lumbar IVDs and vertebral bodies Yes To automate the process of grading lumbar IVDs and vertebral bodies from MRIs. The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model was able to produce predictions of multiple pathological grading that consistently matched those of the radiologist. The system could be beneficial in aiding clinical diagnoses in terms of objectivity of grading and the speed of analysis.
Wang et al. [15] 2017 China A set of 26 cases A set of 26 cases Deep Siamese neural networks Spinal metastasis NR A multi-resolution approach for spinal metastasis detection in MRI The results showed that the proposed approach correctly detects all the spinal metastatic lesions. The results indicated that the proposed Siamese neural network method, combined with the aggregation strategy, provided a viable strategy for the automated detection of spinal metastasis in MRI images.
Sari et al. [11] 2010 TCirkiy 169 169 MLP: the designed ANN consisted of feed-forward back propagation, two hidden layers LBP NR Comparison of ANNs and adaptive neuro-fuzzy inference system for the assessment of the LBP The results showed that the ANNs and adaptive neuro-fuzzy inference system behave very similar to real data. The use of these systems can be used to successfully diagnose the back pain intensity.
Veronezi et al. [12] 2015 Brazil 68 Radiographies for the training stage 68 Images for tests and 70 for validation Neural networks Osteoarthritis of the lumbar spine NR For the diagnosis of osteoarthritis of the lumbar spine The validation was carried out on the best results, achieved accuracy of 62.85%, sensitivity of 65.71%, and specificity of 60%. Although the neural network presented an average efficacy, because this was an innovative study, its results showed a potential for the use of computer-based artificial neural networks to assist and support practitioners.
Zhang et al. [13] 2017 China 235 Radiographs 105 Radiographs DNN Scoliosis assessment Yes To perform automatic measurements of Cobb angle for scoliosis assessment The differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5∘. The variability of Cobb angle measurements could be reduced if the DNN system was trained with enough vertebral patches.
Jamaludin et al. [14] 2017 UK 90% in a training set of 1,806 patients 10% in an independent sample of 203 patients CNN Lumbar IVDs and vertebral bodies Yes To automate the process of grading lumbar IVDs and vertebral bodies from MRIs. The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model was able to produce predictions of multiple pathological grading that consistently matched those of the radiologist. The system could be beneficial in aiding clinical diagnoses in terms of objectivity of grading and the speed of analysis.
Wang et al. [15] 2017 China A set of 26 cases A set of 26 cases Deep Siamese neural networks Spinal metastasis NR A multi-resolution approach for spinal metastasis detection in MRI The results showed that the proposed approach correctly detects all the spinal metastatic lesions. The results indicated that the proposed Siamese neural network method, combined with the aggregation strategy, provided a viable strategy for the automated detection of spinal metastasis in MRI images.
Kim et al. [16] 2018 USA 15,840 6,789 ANNs Posterior lumbar spine fusion Yes Comparison of ANNs, LR, and ASA class to identify risk factors of developing complications following posterior lumbar spine fusion ANN and LR both outperformed ASA class for predicting all four types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality. In summary, machine learning in the form of LR and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.
Kim et al. [17] 2018 South Korea Total training epoch was 200 The experiments were done using 5-fold cross validation and each experiment had 5 test images and 20 training images. CNN IVDs Yes To segmentation of the IVDs from MR spine images The proposed network achieved 3% higher DSC than conventional U-net for IVD segmentation (89.44% vs. 86.44%, respectively; p<0.001). For IVD boundary segmentation, the proposed network achieved 10.46% higher DSC than conventional U-net (54.62% vs. 44.16%, respectively; p<0.001).
Kim et al. [18] 2018 South Korea Four-fold cross validation on a patient-level independent split Four-fold cross validation on a patient-level independent split DCNN Tuberculous and pyogenic spondylitis Yes To differentiate between tuberculous and pyogenic spondylitis on MR imaging, compared to the performance of skilled radiologists When comparing the AUC value of the DCNN classifier (0.802) with the pooled AUC value of the three readers (0.729), there was no significant difference (p=0.079). In differentiating between tuberculous and pyogenic spondylitis using MR images, the performance of the DCNN classifier was comparable to that of three skilled radiologists.
Han et al. [19] 2018 Canada The dataset comprises 253 lumbar scans from 253 patients The dataset comprises 253 lumbar scans from 253 patients Recurrent neural network IVDs, vertebrae, and neural foraminal stenosis NR To perform automated segmentation and classification (i.e., normal and abnormal) of IVDs, vertebrae, and neural foramen in MRIs Extensive experiments on MRIs of 253 patients have demonstrated that Spine-GAN achieved high pixel accuracy of 96.2%, Dice coefficient of 87.1%, sensitivity of 89.1%, and specificity of 86.0%, which revealed its effectiveness and potential as a clinical tool.
Chmelik et al. [20] 2018 Czechia Dataset consisted of 120,000 samples in total, in 31 cases Dataset consisted of 120,000 samples in total, in 31 cases DCNN Metastatic spinal lesions Yes To address the segmentation and classification to define metastatic spinal lesions in 3D CT data Algorithm enables detection, segmentation and classification of small lesions greater than 1.4 mm3 (with diameter greater than 0.7 mm) and works also with cervical vertebrae not treated in other considered methods for spinal analysis of CT scans.
Liao et al. [21] 2018 USA 242 CT scans from 125 patients are used for training 60 CT scans for testing Deep learning, CNN, recurrent neural network, multi-task learning Vertebrae NR To automatically vertebrae identification and localization in spinal CT images The experimental results showed that approach outperforms the state-of-the-art methods by a significant margin.
Al Arif et al. [22] 2018 UK 124 X-ray images 172 Images CNN Cervical vertebrae NR To automatically framework for segmentation of cervical vertebrae in X-ray images A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved. The framework could take an X-ray image and produce a vertebrae segmentation result without any manual intervention.
Han et al. [23] 2018 China 160 (80%) 40 (20%) DMML-Net LNFS NR To automatically pathogenesis-based diagnosis of lumbar neural foraminal stenosis DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This method showed an efficient tool for clinical LNFS diagnosis.
Li et al. [24] 2018 China Voxel changes for each IVD in 12 subjects within 2 time points Voxel changes for each IVD in 12 subjects within 2 time points FCN IVDs Yes To automatically localization and segmentation of IVDs from multi-modality 3D MR data Algorithm achieved state-of-the-art IVD segmentation performance from multimodality images. Compared with network trained with single-scale context image, the proposed 3D multi-scale FCN could generate features with high discrimination capability.
Zhou et al. [25] 2019 China The dataset contains 4,417 videos The dataset contains 4,417 videos Deep learning Lumbar vertebras NR To automatically detect lumbar vertebras in MRI images Algorithm achieved the accuracy of 98.6% and the precision of 98.9%. Most failed results were involved with wrong S1 locations or missed L5. The study demonstrated that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images.
Wang et al. [26] 2019 China Data set of 98 spine CT scans Data set of 98 spine CT scans Combining deep stacked sparse autoencoder contextual features and structured regression forest Vertebrae Yes To automatically vertebra localization and identification from CT Compared with the hidden Markov model and the method based on CNN, the proposed approach could effectively and automatically locate and identify spinal targets in CT scans, and achieve higher localization accuracy, low model complexity
Lessmann et al. [27] 2019 Netherland Five diverse datasets, including multiple modalities (CT and MR) Five diverse datasets, including multiple modalities (CT and MR) CNN Vertebrae Yes To automatically vertebra segmentation and identification The anatomical identification had an accuracy of 93%. Vertebrae were classified as completely or incompletely visible with an accuracy of 97%. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible, and generalizable.
Lang et al. [28] 2019 China A total of 61 patients with clinical spinal MRI database with a DCE sequence A total of 61 patients (30 lung cancers and 31 non-lung cancers) CNN Spinal metasta-ses originated from lung and other cancers Yes To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers Classification using CNN achieved a mean accuracy of 0.71±0.043, whereas a convolutional long short-term memory improved accuracy to 0.81±0.034. DCE-MRI machinelearning analysis methods had potential to predict lung cancer metastases in the spine.
Galbusera et al. [29] 2019 Italy 443 50 Deep learning approach To extract anatomical parameters from biplanar radiographs of the spine NR To automatically determine the shape of the spine The standard errors of the estimated parameters ranged from 2.7° (for the pelvic tilt) to 11.5° (for the L1-L5 lordosis). The proposed method was able to automatically determine the spine shape in biplanar radiographs and calculate anatomical and posture parameters in a wide scenario of clinical conditions with a very good visual performance.
Hopkins et al. [30] 2019 USA 78 26 ANN CSM NR (1) To predict CSM diagnosis; and (2) to predict CSM severity Median accuracy of model was 90.00%. Machine learning provided a promising method for prediction, diagnosis, and even prognosis in patients with CSM.
Horng et al. [31] 2019 Taiwan 35 Images captured from young scoliosis. The dataset consisted of 595 vertebra images 35 Images captured from young scoliosis CNN approach Cobb angle measurement of Spine Yes To automatically measure spine curvature using the anterior-posterior view spinal X-ray images The segmentation results of the Residual UNet were superior to the other two CNNs. The proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.
Pang et al. [32] 2019 China T1-weighted MR images of 215 subjects and T2-weighted MR images of 20 subjects T1-weighted MR images of 215 subjects and T2-weighted MR images of 20 subjects Cascade amplifier regression network Spine NR To automatically quantitative measurement of the spine (i.e., multiple indices estimation of heights, widths, areas, and so on for the vertebral body and disc) The proposed approach achieved impressive performance with mean absolute errors of 1.22±1.04 mm and 1.24±1.07 mm for the 30 lumbar spinal indices estimation of the T1-weighted and T2-weighted spinal MR images, respectively. The proposed method showed a great potential in clinical spinal disease diagnoses and assessments.
Li et al. [33] 2019 China 120 Cases were used for experiments 120 Cases were used for experiments DNN To paraspinal muscle segmentation NR To automatically segmentation of the paraspinal muscle in MRI The experimental results show that the model can achieve higher predictive capability. The dice coefficient of the multifidus is as high as 0.949, and the Hausdorff distance is 4.62 mm. The proposed method can quickly calculate the cross-sectional area of the paraspinal muscles, which provides a convenient condition for doctors to screen sarcopenia and also quantify the changes of paraspinal muscles before and after lumbar spine surgery.
Chen et al. [34] 2019 China End-to-end training at the spine level is proposed to allow the FCN to directly learn the long-range image patterns from full-size CT volumes End-to-end training at the spine level is proposed to allow the FCN to directly learn the long-range image patterns from full-size CT volumes FCN Vertebrae identification and localization NR To automatically identification and localization of vertebrae in spinal CT imaging The proposed framework was quantitatively evaluated on the public dataset from the MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification and demonstrates an identification rate (within 20 mm) of 94.67%, a mean identification rate of 87.97%, and a mean error distance of 2.56 mm on the test set, thus achieving the highest performance reported on this dataset.
Rak et al. [35] 2019 Germany The first whole spine images of 64 subjects were contained. The second 23. The first whole spine images of 64 subjects were contained. The second 23. Combining CNNs and star convex cuts Whole spine segmentation by MRI Yes To automatically approach for fast vertebral body segmentation in 3D MRI of the whole spine Complete whole spine segmentation took 32.4±1.92 seconds on average. Results were superior to those of previous works at a fraction of their run time, which illustrated the efficiency and effectiveness of their whole spine segmentation approach.
Pan et al. [36] 2019 China Cobb angles on 248 chest X-rays were measured automatically using a computer-aided method Cobb angles on 248 chest X-rays were measured automatically using a computer-aided method The Cobb angle of the spinal curve was measured from the output of the Mask R-CNN models Spine alignment assess Yes To automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed Intraclass correlation coefficient between the computer-aided and manual methods for Cobb angle measurement was 0.854. These results indicated that the computer-aided method had good reliability for Cobb angle measurement on chest X-rays. In conclusion, the computer-aided method has potential for automatic Cobb angle measurement and scoliosis diagnosis on chest X-rays.
Weng et al. [37] 2019 Taiwan The ResUNet was trained and evaluated on 990 standing lateral radiographs The ResUNet was trained and evaluated on 990 standing lateral radiographs CNN Spine alignment assess Yes To develop a CNN tools for measuring the SVA from lateral radiography of whole spine for key point detection (ResUNet) The SVA calculation takes approximately 0.2 seconds per image. The intra-class correlation coefficient of the SVA estimates between the algorithm and physicians of different years of experience ranges from 0.946 to 0.993, indicating an excellent consistency. The superior performance of the proposed method and its high consistency with physicians proved its usefulness for automatic measurement of SVA in clinical settings.
Huang et al. [38] 2019 China 50 Sets lumbar MRIs 50 Sets lumbar MRIs DL Vertebrae and IVDs on lumbar spine NR To develop a DL based program (Spine Explorer) for automated segmentation and quantification of the vertebrae and IVDs on lumbar spine MRIs The trained Spine Explorer automatically segments and measures a lumbar MRI in half a second, with mean intersection-overunion of 94.7% and 92.6% for the vertebra and disc, respectively. Spine Explorer was an efficient, accurate, and reliable tool to acquire comprehensive quantitative measurements for lumbar vertebra and disc. Implication of such deep learning-based program can facilitate clinical studies of the lumbar spine.
Jakubicek et al. [39] 2019 Czech Republic 130 CT scans 130 CT scans Two CNNs together with a spine tracing algorithm Spine-ends and spine centerline delimitation assessment are important in many spine diagnostic tasks NR To develop a CNN to automatic spine centerline detection in CT data Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.
Lyu et al. [40] 2019 China 75 Groups imaging data 75 Groups imaging data CNN To assessment of spine scoliosis by Sco-lioscan from 3D ultrasound Yes To develop a CNN to select the best ultrasound images automatically, and compare with the classification method of DenseNet. The result showed that the proposed CNN achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the CNN was more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.
Watanabe et al. [41] 2019 Japan 1 0,788 Moire image-radiograph pairs 198 Moire image-radiograph pairs CNN To assessment of spine scoliosis NR To create a scoliosis screening system that estimates spinal alignment, the Cobb angle, and vertebral rotation from moire images. The proposed method of estimating the Cobb angle and the angle of virtual reality from moire images using a CNN was expected to enhance the accuracy of scoliosis screening.
Kok et al. [42] 2019 Türkiy 300 Individuals aged between 8 and 17 years 300 Individuals aged between 8 and 17 years k-NN, NB, Tree, ANN, SVM, RF, and LR algorithms were used. CVS Yes To determine CVS for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other kNN and LR algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.
Iriondo et al. [43] 2020 USA 38 Scans from 31 unique patients, with a total of 80 segmented slices 20 Segmented slices CNN to segment lumbar IVDs by MRI Lumbar IVDs NR To assess associations between disc degeneration, disability, and LBP This work presented a scalable pipeline for fast, automated assessment of disc relaxation times, and voxel-based relaxometry that overcomes limitations of current region of interest-based analysis methods and may enable greater insights and associations between disc degeneration, disability, and LBP.
Lee et al. [45] 2020 South Korea 233 101 Deep convolutional networks To identify individuals with abnormal BMD from spine X-ray images NR To analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations. A combination of feature extraction was found, by VGGnet and classification by random forest based on the maximum BCR yielded the best performance in terms of the AUC (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73). Finally, the combination for the best performance in predicting high-risk populations with abnormal BMD was identified.
Won et al. [44] 2020 South Korea 542 L4-5 axial MR images 542 L4-5 axial MR images DCNN To identify spine stenosis grading from MRI Yes To compare the diagnostic agreement between the experts and trained artificial CNN classifiers. Final agreement between the expert and the model trained with the labels of the expert was 77.9% and 74.9%, and the differences between each expert and the trained models were not significant. They were concluded that automatic diagnosis using deep learning may be feasible for spinal stenosis grading.
Lee et al. [46] 2020 South Korea 280 Pairs of lumbar spine CT scans and MR T2 images 15 Pairs of lumbar spine CT scans and MR T2 images GANs To diagnosis of spine disease Yes To apply GANs, to synthesize spine MR images from CT images The mean overall similarity of the synthetic MR T2 images evaluated by radiologists was 80.2%. Synthesis of MR images from spine CT images using GANs will improve the spine diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.
Bae et al. [47] 2020 South Korea Patients (N=17, 1,684 slices) Healthy controls (N=24, 3,490 slices) CNN Cervical spine Yes To identify superior and inferior vertebrae in a single slice of CT images, and a post-processing for 3D segmentation and separation of cervical vertebrae The results demonstrated that automated method achieved comparable accuracies with inter- and intra-observer variabilities of manual segmentation by human experts, which is time consuming.
Jakubicek et al. [48] 2020 Czech Republic The more samples, the more accurate The more samples, the more accurate CNN Incomplete spines assessment in patients with bone metastases and vertebral compression by CT imag-ing NR To localization and iden-tification of vertebrae in 3D CT scans of possibly incomplete spines in patients with bone metastases and vertebral compressions The proposed framework, which combined several advanced methods including also three CNNs, worked fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis system for automatic spine-bone lesion analysis in oncological patients.
Kim et al. [49] 2020 South Korea 330 CT images 14 CT images CNN for segmentation To diagnosis of back pain Yes To improve diagnosis of back pain by spine segmentation in CT scans using algorithmic methods The CNN method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%. The proposed CNN approach can be very practical and accurate for spine segmentation as a diagnostic method.
Rehman et al. [50] 2020 Pakistan 25 CT image data (both healthy and fractured cases) 25 CT image data (both healthy and fractured cases) A novel combination of traditional region-based level set with deep learning framework To diagnosis of osteoporotic fractures by vertebral bone segmentation NR To predict shape of vertebral bones accurately Dice score was found to be 96.4%±0.8% without fractured cases and 92.8%±1.9% with fractured cases in dataset (lumber and thoracic). The proposed technique outperformed other state-of-the-art techniques and handled the fractured cases for the first time efficiently.

LVQ was used for quantizing the learning data to feed them to ANN.

ANN, artificial neural network; MLP, multilayer perceptron neural networks; LBP, low back pain; NR, not reported; LVQ, learning vector quantization; DNN, deep neural network; CNN, convolutional neural network; IVD, intervertebral disc; MRI, magnetic resonance imaging; LR, logistic regression; ASA, American Society for Anesthesiology; MR, magnetic resonance; DSC, Dice similarity coefficient; DCNN, deep convolutional neural network; AUC, area under the curve; 3D, three-dimensional; CT, computed tomography; DMML-Net, deep multiscale multitask learning network; LNFS, lumbar neural foraminal stenosis; FCN, fully convolutional networks; DCE, dynamic contrast enhanced; CSM, cervical spondylotic myelopathy; SVA, sagittal vertical axis; DL, deep learning; k-NN, k-nearest neighbors; NB, Naive Bayes; Tree, decision tree; SVM, support vector machine; RF, random forest; CVS, cervical vertebrae stages; BMD, bone mineral density; BCR, balanced classification rate; GANs, generative adversarial networks.

Table 2.
A list of papers on ANN used in spine prognosis
Author Year Country No. of sample size
Model type Conditions Comparison with non-ANN models Main focus Results/conclusion(s)
Training Testing
Dickey et al. [51] 2002 USA 157 29 MLP: three-layer ANNs were used with 32 inputs, one hidden layer and one output LBP Yes To investigate the relationship between intervertebral motion, intravertebral deformation, and pain in chronic LBP patients The neural network model showed a strong relationship between observed and predicted pain (r=0.997). ANNs are able to effectively describe relationships between pain and vertebral motion in chronic LBP.
Lin et al. [52] 2008 Taiwan 1,126 375 MLP Spine Yes Comparison of ANNs and LR to identify patients with high risk of hypotension during spinal anesthesia The ANN model had a sensitivity of 75.9% and specificity of 76.0%. The LR model had a sensitivity of 68.1% and specificity of 73.5%. The area under receiver operating characteristic curves were 0.796 and 0.748. The ANN model performed significantly better than the LR model. The prediction of clinicians had the lowest sensitivity of 28.7%, 22.2%, 21.3%, 16.1%, and 36.1%, and specificity of 76.8%, 84.3%, 83.1%, 87.0%, and 64.0%.
Parsaeian et al. [53] 2012 Iran 17,294 17,295 MLP: a three-layer perceptron with nine inputs, three hidden and one output neurons was employed LBP Yes To compare empirically predictive ability of an artificial neural network with a LR in prediction of LBP The area under the ROC curve (SE), root mean square, and -2loglikelihood of the logistic regression was 0.752 (0.004), 0.3832, and 14,769.2, respectively. The area under the ROC curve (SE), root mean square and -2log-likelihood of the artificial neural network was 0.754 (0.004), 0.3770, and 14,757.6, respectively. ANNs would give better performance than LR.
Papić et al. [54] 2016 Serbia Data set included 145 patients, and 10-fold cross validation 10-Fold cross validation The classification problem was approached using decision trees, SVM and MLP combined with RELIEF algorithm for feature selection. LDH Yes To predict the return to work after operative treatment of LDH MLP provided best recall of 0.86 for the class of patients not returning to work. The predictive modeling indicated at the most decisive risk factors in prolongation of work absence: psychosocial factors, mobility of the spine and structural changes of facet joints and professional factors including standing, sitting, and microclimate.
Kim et al. [16] 2018 USA 15,840 (70%) 6,789 (30%) ML Posterior lumbar spine fusion surgery Yeas To automatically predict (identify risk factors for) complications following posterior lumbar spine fusion and compared with regression model (LR) Though ML and LR had comparable AUC values for predicting all types of complications as cardiac complications, wound complications, venous thromboembolism, and mortality. However, ANN had greater sensitivity than LR for detecting wound complications and mor-tality. ML and LR were more accurate than benchmark ASA scores
Karhade et al. [55] 2018 USA 21,091 5,273 ML algorithms Lumbar degenerative disc NR To use ML to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders The rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Machine learning algorithms showed promising results on internal validation for preoperative prediction of nonroutine discharges.
Arvind et al. [56] 2018 USA 14,615 Patients 6,264 ANN, LR, SVM, and RF models Cervical discectomy Yes To develop predictive algorithms for postoperative complications following anterior cervical discectomy and fusion The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p<0.05). ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications.
Han et al. [57] 2019 USA 355,607 (70%) 152,403 (30%) MLA Spine surgery Yes To develop and evaluate a set of predictive models for common adverse events after spine surgery The predictive models for adverse events following spine surgery built based on this data showed greater accuracy versus the previous models, with AUC ranging between 0.7 and 0.76, which account for patient-, diagnosis-, and procedure-related factors.
DeVries et al. [58] 2019 Canada 862 Patients included that walk (n=323) not walk (n=318) 862 Patients included MLA tSCI Yes To automatically prognosticate walking recovery in patients with tSCI and compared with LR MLAs had comparable prognostication as the previously reported models. Overall, no relevant differences were found between the models suggesting that using a more sophisticated MLA and a greater amount of neurological data does not improve the prediction of walking recovery in tSCI patients.
Staartjes et al. [59] 2019 Netherland A total of 422 were included and data training, sets was 60%. Data validation, and test sets was in a 20%/20% ratio. Deep learning-based analytics LDH Yes To evaluate a clinically relevant improvement in leg pain, back pain, and functional disability after LDH surgery by deep learning and compared with regression model After 1 year, 337 (80%), 219 (52%), and 337 (80%) patients reported a clinically relevant improvement in leg pain, back pain, and functional disability, respectively. The regression models provided inferior performance measures for each of the outcomes. The study demonstrated that generating personalized and robust deep learning-based analytics for outcome prediction was feasible even with limited amounts of center-specific data.
Karhade et al. [60] 2019 USA 1,432 (80%) 358 (20%) MLA Spinal metastatic disease NR To automatically predict 30-day mortality of patients undergoing surgery for spinal metastatic disease The 30-day mortality for the 1,790 patients undergoing surgery for spinal metastatic disease was 8.49%. MLAs were promising for prediction of postoperative outcomes in spinal oncology and these algorithms could be integrated into clinically useful decision tools.
Karhade et al. [61] 2019 USA 587 (80%) 145 (20%) Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) To develop predictive algorithms for spinal metastatic disease NR To automatically predict 90-day and 1-year mortality in spinal metastatic disease Overall, 732 patients were identified with 90-day and 1-year mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms.
Weber et al. [62] 2019 USA Train and test a CNN for muscle segmentation and automatic money flow index calculation were performed using high resolution fat-water images from 39 participants Train and test were performed using high resolution fat-water images from 39 participants Deep learning CNN models Muscle fat infiltration following whiplash injury in cervical spine NR To automatically quantification of muscle fat infiltration following whiplash injury Overall, CNN's may improve d the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.
Goyal et al. [63] 2019 USA A total of 59,145 cases wer e analyzed. The best combination selected by a 10-fold cross-validation procedure. 10-Fold cross-validation procedure ML algorithms Spinal fusion surgery NR To develop algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC >0.80; range, 0.85-0.87) for predicting nonhome discharge. Multiple ML algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions
Karhade et al. [64] 2019 USA 4,331 (80%) 1,082 (20%) ML algorithms LDH Yes To develop algorithms for prediction of prolonged opioid prescription after surgery for LDH Overall, 5,413 patients were identified, with sustained postoperative opioid prescription of 416 (7.7%) at 90 to 180 days after surgery. The elastic-net penalized logistic regression model had the best discrimination (c-statistic 0.81) and good calibration and overall performance. They showed that preoperative prediction of prolonged postoperative opioid prescription with this model can help identify candidates for increased surveillance after surgery.
Ryu et al. [65] 2020 South Korea 870 218 RED_SNN: final network consists of embedding layer, long short-term memory layer, four fully connected layers. Spino-pelvic chondrosarcoma Yes To predict survival following a spino-pelvic chondrosarcoma diagnosis The median c-index of the five validation sets was 0.84 (95% confidence interval, 0.79–0.87).

ANN, artificial neural network; MLP, multilayer perceptron neural networks; LBP, low back pain; LR, logistic regression; ROC, receiver operating characteristic; SE, standard error; SVM, support vector machine; LDH, lumbar disc herniation; ML, machine learning; NR, not reported; AUC, area under the curve; ASA, American Society for Anesthesiology; NR, not reported; RF, random forest decision tree; MLA, machine learning algorithms; tSCI, traumatic spinal cord injury; CNN, convolutional neural network; RED_SNN, risk estimate distance survival neural network.

Table 3.
A list of papers on ANN used in spine outcome prediction
Author Year Country No. of sample size
Model type Conditions Comparison with non-ANN models Main focus Results/conclusion(s)
Training Testing
Azimi et al. [3] 2014 Iran 84 84 ANN model LSS Yes To develop an ANN model for predicting 2-year surgical satisfaction, and to compare the new model with traditional predictive tools in patients with lumbar spinal canal stenosis The ANN model displayed a better accuracy rate in 96.9% of patients, a better Hosmer-Lemeshow statistic in 42.4% of patients, and a better receiver operating characteristic-AUC in 80% of patients, compared with the LR model. ANNs can predict 2-year surgical satisfaction in LSS patients with a high level of accuracy.
Azimi et al. [66] 2015 Iran 201 201 ANN model Recurrent LDH Yes To develop an ANN model to predict recurrent LDH Compared with the LR model, the ANN model was associated with superior results: accuracy rate, 94.1%; H-L statistic, 40.2%; and AUC, 0.83% of patients. ANNs could be used to predict the diagnostic statues of recurrent and nonrecurrent group of LDH patients before the first or index microdiscectomy.
Azimi et al. [4] 2016 Iran 102 101 ANN model LDH Yes To develop an ANNs model for predict successful surgery outcome in LDH Compared to the LR model, the ANN model showed better results: accuracy rate, 95.8%; H-L statistic, 41.5%; and AUC, 0.82% of patients. ANNs can predict successful surgery outcome with a high level of accuracy in LDH patients.
Azimi et al. [67] 2017 Iran 174 86 ANN model LSCS Yes To accurately select patients for surgery or non-surgical options and to compare such with the traditional clinical decisionmaking approach in LSCS patients The ANN model displayed better accuracy rate (97.8%), a better H-L statistic (41.1%) which represented a good-fit calibration, and a better AUC (89.0%), compared to the LR model. ANN model could predict the optimal treatment choice for LSCS patients in clinical setting and is superior to LR model.
Karhade et al. [68] 2019 USA 844 (80%) 209 (20%) ML algorithm SEA NR To develop ML algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. ML algorithms showed promise on internal validation for prediction of 90-day mortality in SEA.
Stopa et al. [69] 2019 USA 144 Patients 144 Patients ML algorithm Lumbar disc disorders surgery NR To predict nonroutine discharge for patients undergoing surgery for lumbar disc disorders A nonroutine discharge rate of 6.9% (n=10). The neural network algorithm generalized well to the institutional data, with a c-statistic of 0.89. ML showed that a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge,
Zhang et al. [70] 2019 China 58 22 ML Lumbar vertebral strength of elderly men NR To predict vertebral strength based on clinical quantitative computed tomography images by using machine learning High accuracy was achieved to predict vertebral strength. This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.
Hopkins et al. [71] 2019 USA 17,448 5,816 DNN Spinal fusions NR To develop an AI model to predict 30-day readmissions after posterior lumbar fusion Mean positive predictive value was 78.5%. Mean negative predictive value was 97%. The DNN model was able to predict those patients who would not require readmission.
Hopkins et al. [72] 2020 USA 3,034 1,012 DNN Spinal fusions NR To develop an AI model for predict surgical site infections after posterior spinal fusions The five highest weighted variables were congestive heart failure, chronic pulmonary failure, hemiplegia/ paraplegia, multilevel fusion, and cerebrovascular disease, respectively. Notable factors that were protective against infection were intensive care unit admission, increasing Charlson Comorbidity Index score, race (White), and being male. They reported that AI was relevant and impressive tools that should be employed in the clinical decision making for patients.

ANN, artificial neural network; LSS, lumbar spinal stenosis; AUC, area under the curve; LR, logistic regression; LDH, lumbar disk herniation; H-L statistic, Hosmer-Lemeshow statistic; LSCS, lumbar spinal canal stenosis; ML, machine learning; SEA, spinal epidural abscess; NR, not reported; DNN, deep neural network; AI, artificial intelligence.

Table 4.
A list of papers on ANN used in the biomechanical assessments of spinal disease
Author Year Country No. of sample size
Model type Conditions Comparison with non-ANN models Main focus Results/conclusion(s)
Training Testing
Mann et al. [73] 1993 USA The more samples, the more accurate The more samples, the more accurate MLP Lumbar spine disorder NR To determine the reliability of the patient pain drawing when diagnosing low-back disorders and to delineate the pain mark patterns particular to each disorder by comparing physicians with computerized methods The physicians averaged 51% accuracy with individual preferences for certain disorder groups. The computerized methods demonstrated comparable accuracy (48%) and more agreement in classification. ANNs was useful to clinicians for making accurate predictions of diagnosis from pain drawings.
Ongphiphadhanakul et al. [74] 1997 Thailand 100 29 MLP Low BMD NR To evaluate the risk factors associated with low BMD and assess the prediction of low BMD using an ANN compared to a LR There was no significant difference in terms of accuracy, sensitivity, and specificity in the prediction of low BMD at the lumbar spine or the femoral neck between ANN model and LR model. Results showed that ANN did not perform better than convention statistical methods in the prediction of low BMD.
Nussbaum et al. [75] 1997 USA The more samples, the more accurate The more samples, the more accurate MLP Lumbar muscle recruitment during static loading NR To examine inter-individual differences in the patterns of torso muscle recruitment during 3D static moment loading of the lumbar spine. It was speculated that inter individual muscle recruitment differences may be important for assessing individual musculoskeletal risk.
Wang et al. [76] 2002 USA The EMG signals of 10 flexor and extensor muscles The EMG signals of 10 flexor and extensor muscles MLP Joint moments NR To determine muscle activations from EMG signals. The results showed that the neural network model can be used to represent the relationship between EMG signals and joint moments well.
Arjmand et al. [77] 2013 Iran 5,220 Load positions and the more samples, the more accurate The more samples, the more accurate Five-layer, feedforward neural network model Spinal loads and muscle forces Yes Two ANNs was constructed, trained, and tested to map inputs of a complex trunk FE model to its outputs for spinal loads and muscle forces and compared to regression equations. Results indicated that the ANNs were more accurate in mapping input-output relationships of the FE model (RMSE=20.7 N for spinal loads and RMSE=4.7 N for muscle forces) as compared to regression equations (RMSE=120.4 N for spinal loads and RMSE=43.2 N for muscle forces). Using these user-friendly tools, spine loads and trunk muscle can be easily estimated.
Amaritsakul et al. [78] 2013 Taiwan 25 Screw designs were used as the learning set. 10 Randomly selected screw designs MLP: a threelayered ANN Optimization design of spinal pedicle screws Yes Using the 3D FE analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an ANN algorithm, and the trade-off solutions known as Pareto optima were explored by a GA. Multi-objective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously.
Hu et al. [79] 2018 USA 44 Chronic LBP and healthy individuals 44 Chronic LBP and healthy individuals Deep neural networks LBP NR To recognize LBP patients from healthy population performed static standing tasks, while their spine kinematics and center of pressure were recorded. Results indicated that deep neural networks could recognize LBP populations with precision up to 97.2%. Results showed a deep learning network can solve the above classification problem with both promising precision and recall performance.
Aghazadeh et al. [80] 2019 Iran 15 Individuals each performed 135 load-handling activities 15 Individuals each performed 135 load-handling activities Coupled ANNs The risk of spine injury during manual material handling NR To estimate 3D whole-body posture, lumbosacral moments, and spinal loads during load-handling activities The results showed outputs of the coupled ANNs for L4-L5 IDPs during a number of activities were in agreement with measured IDPs. Hence, coupled ANNs were found to be robust tools to evaluate posture, lumbosacral moments, spinal loads, and thus risk of injury during load-handling activities.
Liu et al. [81] 2019 China The model was trained for 20 epochs with a mini-batch size of 16. The model was trained for 20 epochs with a mini-batch size of 16. CSNN Tracking the motion of the lumbar spine NR To automatically track lumbar vertebras with rotated bounding boxes in digitalized video fluoroscopic imaging, sequences. Results indicated that the proposed tracking method can track the lumbar vertebra steadily and robustly. The study demonstrated that the lumbar tracker based on CSNN can be trained successfully without annotated lumbar sequences.
Zhang et al. [70] 2019 China 80 Subjects with QCT data of lumbar spine were randomly selected 80 Subjects with QCT data of lumbar spine were randomly selected Machine learning models o predict vertebral strength NR The parameters extracted from QCT images were used to predict vertebral strength through machine learning models. The 58 parameters were simplified to five features and nine PCs. High accuracy was achieved by using the five features or the nine PCs to predict vertebral strength. This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.

ANN, artificial neural network; MLP, multilayer perceptron neural networks; NR, not reported; BMD, bone mineral density; LR, logistic regression; 3D, three-dimensional; EMG, electromyogram; FE, finite element; RMSE, root mean square error; GA, genetic algorithm; LBP, low back pain; IDP, intradiscal pressure; CSNN, convolutional siamese neural network; QCT, quantitative computed tomography; PCs, principal components.

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