A deep learning model for detection of cervical spinal cord compression in MRI scans

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Automated detection, labelling and radiological grading of clinical spinal MRIs

Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model's grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.

The Success of Deep Learning Modalities in Evaluating Modic Changes

BACKGROUND

Modic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities.

METHODS

The sagittal T1, sagittal and axial T2-weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19-86 years, who underwent MRI examination between 2016-2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of 2 independent stages: classification and segmentation. The categorized data were first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U-Net, which is the CNN architecture, with image processing programs on the marked images.

RESULTS

During the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U-Net architecture.

CONCLUSIONS

Evaluation of MRI findings of MC in the etiology of lower back pain with deep learning architectures can significantly reduce the workload of the radiologist by providing ease of diagnosis.

A Convolutional Neural Network for Automated Detection of Cervical Ossification of the Posterior Longitudinal Ligament using Magnetic Resonance Imaging

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

We aimed to develop and validate a convolutional neural network (CNN) model to distinguish between cervical ossification of posterior longitudinal ligament (OPLL) and multilevel degenerative spinal stenosis using Magnetic Resonance Imaging (MRI) and to compare the diagnostic ability with spine surgeons.

SUMMARY OF BACKGROUND DATA

Some artificial intelligence models have been applied in spinal image analysis and many of promising results were obtained; however, there was still no study attempted to develop a deep learning model in detecting cervical OPLL using MRI images.

MATERIALS AND METHODS

In this retrospective study, 272 cervical OPLL and 412 degenerative patients underwent surgical treatment were enrolled and divided into the training (513 cases) and test dataset (171 cases). CNN models applying ResNet architecture with 34, 50, and 101 layers of residual blocks were constructed and trained with the sagittal MRI images from the training dataset. To evaluate the performance of CNN, the receiver operating characteristic curves of 3 ResNet models were plotted and the area under the curve were calculated on the test dataset. The accuracy, sensitivity, and specificity of the diagnosis by the CNN were calculated and compared with 3 senior spine surgeons.

RESULTS

The diagnostic accuracies of our ResNet34, ResNet50, and ResNet101 models were 92.98%, 95.32%, and 97.66%, respectively; the area under the curve of receiver operating characteristic curves of these models were 0.914, 0.942, and 0.971, respectively. The accuracies and specificities of ResNet50 and ResNet101 models were significantly higher than all spine surgeons; for the sensitivity, ResNet101 model achieved better values than that of the 2 surgeons.

CONCLUSION

The performance of our ResNet model in differentiating cervical OPLL from degenerative spinal stenosis using MRI is promising, better results were achieved with more layers of residual blocks applied.

Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification

BACKGROUND AND PURPOSE

Cervical spinal cord compression, defined as spinal cord deformity and severe narrowing of the spinal canal in the cervical region, can lead to severe clinical consequences, including intractable pain, sensory disturbance, paralysis, and even death, and may require emergent intervention to prevent negative outcomes. Despite the critical nature of cord compression, no automated tool is available to alert clinical radiologists to the presence of such findings. This study aims to demonstrate the ability of a vision transformer (ViT) model for the accurate detection of cervical cord compression.

MATERIALS AND METHODS

A clinically diverse cohort of 142 cervical spine MRIs was identified, 34% of which were normal or had mild stenosis, 31% with moderate stenosis, and 35% with cord compression. Utilizing gradient-echo images, slices were labeled as no cord compression/mild stenosis, moderate stenosis, or severe stenosis/cord compression. Segmentation of the spinal canal was performed and confirmed by neuroradiology faculty. A pretrained ViT model was fine-tuned to predict section-level severity by using a train:validation:test split of 60:20:20. Each examination was assigned an overall severity based on the highest level of section severity, with an examination labeled as positive for cord compression if ≥1 section was predicted in the severe category. Additionally, 2 convolutional neural network (CNN) models (ResNet50, DenseNet121) were tested in the same manner.

RESULTS

The ViT model outperformed both CNN models at the section level, achieving section-level accuracy of 82%, compared with 72% and 78% for ResNet and DenseNet121, respectively. ViT patient-level classification achieved accuracy of 93%, sensitivity of 0.90, positive predictive value of 0.90, specificity of 0.95, and negative predictive value of 0.95. Receiver operating characteristic area under the curve was greater for ViT than either CNN.

CONCLUSIONS

This classification approach using a ViT model and rules-based classification accurately detects the presence of cervical spinal cord compression at the patient level. In this study, the ViT model outperformed both conventional CNN approaches at the section and patient levels. If implemented into the clinical setting, such a tool may streamline neuroradiology workflow, improving efficiency and consistency.

Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network

Introduction

Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging. Deep learning, a form of artificial intelligence, is most commonly used in medical imaging data analysis. It is a technique that utilizes neural networks to self-learn and extract features from data to diagnose diseases. However, no deep learning model has been developed to detect vertebral diseases in canine thoracolumbar and lumbar lateral X-ray images. Therefore, this study aimed to establish a segmentation model that automatically recognizes the vertebral body and spondylosis deformans in the thoracolumbar and lumbar lateral radiographs of dogs.

Methods

A total of 265 thoracolumbar and lumbar lateral radiographic images from 162 dogs were used to develop and evaluate the deep learning model based on the attention U-Net algorithm to segment the vertebral body and detect spondylosis deformans.

Results

When comparing the ability of the deep learning model and veterinary clinicians to recognize spondylosis deformans in the test dataset, the kappa value was 0.839, indicating an almost perfect agreement.

Conclusions

The deep learning model developed in this study is expected to automatically detect spondylosis deformans on thoracolumbar and lumbar lateral radiographs of dogs, helping to quickly and accurately identify unstable intervertebral disc space sites. Furthermore, the segmentation model developed in this study is expected to be useful for developing models that automatically recognize various vertebral and disc diseases.

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.

The imaging of cervical spondylotic myeloradiculopathy

This review provides a detailed description of the imaging features of cervical spondylotic myelopathy and radiculopathy, with a focus on MRI. Where relevant, we will outline grading systems of vertebral central canal and foraminal stenosis. Whilst post-operative appearances of the cervical spine are outside the scope of this paper, we will touch on imaging features recognised as predictors of clinical outcome and neurological recovery. This paper will serve as a reference for both radiologists and clinicians involved in the care of patients with cervical spondylotic myeloradiculopathy.

The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications

Background

Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging.

Methods

This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest.

Results

A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated.

Conclusions

Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.

Initial study on an expert system for spine diseases screening using inertial measurement unit

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

STUDY DESIGN

Cross-sectional study.

OBJECTIVE

Validate the diagnostic accuracy of a deep-learning algorithm for cervical cord compression due to degenerative canal stenosis on radiography.

SUMMARY OF BACKGROUND DATA

The diagnosis of degenerative cervical myelopathy is often delayed, resulting in improper management. Screening tools for suspected degenerative cervical myelopathy would help identify patients who require detailed physical evaluation.

MATERIALS AND METHODS

Data from 240 patients (120 with cervical stenosis on magnetic resonance imaging and 120 age and sex-matched controls) were randomly divided into training (n = 198) and test (n = 42) data sets. The deep-learning algorithm, designed to identify the suspected stenosis level on radiography, was constructed using a convolutional neural network model called EfficientNetB2, and radiography and magnetic resonance imaging data from the training data set. The accuracy and area under the curve of the receiver operating characteristic curve were calculated for the independent test data set. Finally, the number of correct diagnoses was compared between the algorithm and 10 physicians using the test cohort.

RESULTS

The diagnostic accuracy and area under the curve of the deep-learning algorithm were 0.81 and 0.81, respectively, in the independent test data set. The rate of correct responses in the test data set was significantly higher for the algorithm than for the physician's consensus (81.0% vs . 66.2%; P = 0.034). Furthermore, the accuracy of the algorithm was greater than that of each individual physician.

CONCLUSIONS

We developed a deep-learning algorithm capable of suggesting the presence of cervical spinal cord compression on cervical radiography and highlighting the suspected levels on radiographic imaging when cord compression is identified. The diagnostic accuracy of the algorithm was greater than that of spine physicians.

LEVEL OF EVIDENCE

Level IV.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Background

There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation).

Methods

A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external).

Results

A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation.

Conclusion

Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications.

Level of Evidence

Level III, retrospective cohort study.

SV, Borchert R, Russo S, Eide PK. Radiological predictors of shunt response in the diagnosis and treatment of idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis

BACKGROUND

Patients with the dementia subtype idiopathic normal pressure hydrocephalus (iNPH) may improve clinically following cerebrospinal fluid (CSF) diversion (shunt) surgery, though the predictors of shunt response remain debated. Currently, radiological features play an important role in the diagnosis of iNPH, but it is not well established which radiological markers most precisely predict shunt responsive iNPH.

OBJECTIVE

To conduct a systematic review and meta-analysis to identify radiological predictors of shunt responsiveness, evaluate their diagnostic effectiveness, and recommend the most predictive radiological features.

METHODS

Embase, MEDLINE, Scopus, PubMed, Google Scholar, and JSTOR were searched for original studies investigating radiological predictors of shunt response in iNPH patients. Included studies were assessed using the ROBINS-1 tool, and eligible studies were evaluated using a univariate meta-analysis.

RESULTS

Overall, 301 full-text papers were screened, of which 28 studies were included, and 26 different radiological features were identified, 5 of these met the inclusion criteria for the meta-analysis: disproportionately enlarged subarachnoid space (DESH), callosal angle, periventricular white matter changes, cerebral blood flow (CBF), and computerized tomography cisternography. The meta-analysis showed that only callosal angle and periventricular white matter changes significantly differentiated iNPH shunt responders from non-responders, though both markers had a low diagnostic odds ratio (DOR) of 1.88 and 1.01 respectively. None of the other radiological markers differentiated shunt responsive from shunt non-responsive iNPH.

CONCLUSION

Callosal angle and periventricular changes are the only diagnostically effective radiological predictors of shunt responsive iNPH patients. However, due to the DORs approximating 1, they are insufficient as sole predictors and are advised to be used only in combination with other diagnostic tests of shunt response. Future research must evaluate the combined use of multiple radiological predictors, as it may yield beneficial additive effects that may allow for more robust radiological shunt response prediction.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

Brain Structural and Functional Dissociated Patterns in Degenerative Cervical Myelopathy: A Case-Controlled Retrospective Resting-State fMRI Study

Background

Previous studies have shown the whole-brain global functional connectivity density (gFCD) and gray matter volume (GMV) alterations in patients with degenerative cervical myelopathy (DCM). However, no study aimed to investigate the associations between the spatial patterns of GMV and gFCD alterations in patients with DCM.

Methods

Structural data and resting-state functional MRI data of 35 DCM patients and 35 matched healthy controls were collected to assess their gFCD and GMV and investigate gFCD and GMV alterations in patients with DCM and their spatial pattern associations.

Results

In our current study, significant gFCD and GMV differences were observed in some regions of the visual system, sensorimotor cortices, and cerebellum between patients with DCM and healthy controls. In our findings, decreased gFCD was found in areas primarily located at the sensorimotor cortices, while increased gFCD was observed primarily within areas located at the visual system and cerebellum. Decreased GMV was seen in the left thalamus, bilateral supplementary motor area (SMA), and left inferior occipital cortices in patients with DCM, while increased GMV was observed in the cerebellum.

Conclusion

Our findings suggest that structural and functional alterations independently contributed to the neuropathology of DCM. However, longitudinal studies are still needed to further illustrate the associations between structural deficits and functional alterations underlying the onset of brain abnormalities as DCM develops.

Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning

Background

Modic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning‐based detection tool for voxel‐wise mapping of MCs.

Methods

Seventy‐five lumbar spine MRI exams that presented with acute‐to‐chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel‐wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T₁‐weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T₁‐ and T₂‐weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule‐based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)‐assisted experiment.

Results

The model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI‐assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p < 0.05).

Conclusions

This deep learning‐based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter‐rater reliability in the assessment of MCs.

Detecting ossification of the posterior longitudinal ligament on plain radiographs using a deep convolutional neural network: a pilot study

BACKGROUND CONTEXT

Its rare prevalence and subtle radiological changes often lead to difficulties in diagnosing cervical ossification of the posterior longitudinal ligament (OPLL) on plain radiographs. However, OPLL progression may lead to trauma-induced spinal cord injury, resulting in severe paralysis. To address the difficulties in diagnosis, a deep learning approach using a convolutional neural network (CNN) was applied.

PURPOSE

The aim of our research was to evaluate the performance of a CNN model for diagnosing cervical OPLL.

STUDY DESIGN AND SETTING

Diagnostic image study.

PATIENT SAMPLE

This study included 50 patients with cervical OPLL, and 50 control patients with plain radiographs.

OUTCOME MEASURES

For the CNN model performance evaluation, we calculated the area under the receiver operating characteristic curve (AUC). We also compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN with those of general orthopedic surgeons and spine specialists.

METHODS

Computed tomography was used as the gold standard for diagnosis. Radiographs of the cervical spine in neutral, flexion, and extension positions were used for training and validation of the CNN model. We used the deep learning PyTorch framework to construct the CNN architecture.

RESULTS

The accuracy of the CNN model was 90% (18/20), with a sensitivity and specificity of 80% and 100%, respectively. In contrast, the mean accuracy of orthopedic surgeons was 70%, with a sensitivity and specificity of 73% (SD: 0.12) and 67% (SD: 0.17), respectively. The mean accuracy of the spine surgeons was 75%, with a sensitivity and specificity of 80% (SD: 0.08) and 70% (SD: 0.08), respectively. The AUC of the CNN model based on the radiographs was 0.924.

CONCLUSIONS

The CNN model had successful diagnostic accuracy and sufficient specificity in the diagnosis of OPLL.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

Background

Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose

To develop a DL model for automated classification of MESCC on MRI.

Materials and Methods

Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.

Results

Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.

Conclusion

A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Deep Learning Approaches for Video Compression: A Bibliometric Analysis

Every data and kind of data need a physical drive to store it. There has been an explosion in the volume of images, video, and other similar data types circulated over the internet. Users using the internet expect intelligible data, even under the pressure of multiple resource constraints such as bandwidth bottleneck and noisy channels. Therefore, data compression is becoming a fundamental problem in wider engineering communities. There has been some related work on data compression using neural networks. Various machine learning approaches are currently applied in data compression techniques and tested to obtain better lossy and lossless compression results. A very efficient and variety of research is already available for image compression. However, this is not the case for video compression. Because of the explosion of big data and the excess use of cameras in various places globally, around 82% of the data generated involve videos. Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches. All newly proposed methods aim to increase performance (reducing bitrate up to 50% at the same data quality and complexity). This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. Scopus and Web of Science are well-known research databases. The results retrieved from them are used for this analytical study. Two types of analysis are performed on the extracted documents. They include quantitative and qualitative results. In quantitative analysis, records are analyzed based on their citations, keywords, source of publication, and country of publication. The qualitative analysis provides information on DL-based approaches for video compression, as well as the advantages, disadvantages, and challenges of using them.

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group (P < 0.05); the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group (P < 0.05). Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm

OBJECTIVE

Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans.

METHODS

All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity.

RESULTS

A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively.

CONCLUSIONS

In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test

Background

Recently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.

Methods

Our retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were &gt;30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.

Results

The average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were &gt;30%; therefore, our AI system passed the Turing test.

Conclusion

Our proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.

Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image

BACKGROUND

Early detection of asbestosis is important; hence, quick and accurate diagnostic tools are essential. This study aimed to develop an algorithm that combines lung segmentation and deep learning models that can be utilized as a clinical decision support system (CDSS) for diagnosing patients with asbestosis in segmented computed tomography (CT) images.

METHODS

We accurately segmented the lungs in CT images of patients examined at Seoul St. Mary's Hospital using a threshold-based method. Lungs with asbestosis and normal lungs were classified by applying the segmented image to the long-term recurrent convolutional network deep learning model. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 score from the test data.

RESULTS

The algorithm developed using the DenseNet201pre-trained model showed excellent performance, with a sensitivity of 0.962, specificity of 0.975, accuracy of 0.970, AUROC of 0.968, and F1 score of 0.961.

CONCLUSIONS

We developed an algorithm with significantly better diagnostic accuracy than a radiologist (0.970 vs. 0.73-0.79). Our developed algorithm is expected to be an excellent support tool if used as a CDSS to diagnose asbestosis using CT images.