Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Imaging of Spondylodiscitis: A Comprehensive Updated Review-Multimodality Imaging Findings, Differential Diagnosis, and Specific Microorganisms Detection

Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles.

Differentiation of Native Vertebral Osteomyelitis: A Comprehensive Review of Imaging Techniques and Future Applications

Native vertebral osteomyelitis, also termed spondylodiscitis, is an antibiotic-resistant disease that requires long-term treatment. Without proper treatment, NVO can lead to severe nerve damage or even death. Therefore, it is important to accurately diagnose the cause of NVO, especially in spontaneous cases. Infectious NVO is characterized by the involvement of 2 adjacent vertebrae and intervertebral discs, and common infectious agents include Staphylococcus aureus, Mycobacterium tuberculosis, Brucella abortus, and fungi. Clinical symptoms are generally nonspecific, and early diagnosis and appropriate treatment can prevent irreversible sequelae. Advances in pathologic histologic imaging have led physicians to look more forward to being able to differentiate between tuberculous and septic spinal discitis. Therefore, research in identifying and differentiating the imaging features of these 4 common NVOs is essential. Due to the diagnostic difficulties, clinical and radiologic diagnosis is the mainstay of provisional diagnosis. With the advent of the big data era and the emergence of convolutional neural network algorithms for deep learning, the application of artificial intelligence (AI) technology in orthopedic imaging diagnosis has gradually increased. AI can assist physicians in imaging review, effectively reduce the workload of physicians, and improve diagnostic accuracy. Therefore, it is necessary to present the latest clinical research on NVO and the outlook for future AI applications.

Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects

Nuclear medicine has become an indispensable discipline in the diagnosis and management of musculoskeletal infections. Radionuclide tests serve as a valuable diagnostic tool for patients suspected of having osteomyelitis, spondylodiscitis, or prosthetic joint infections. The choice of the most suitable imaging modality depends on various factors, including the affected area, potential extra osseous involvement, or the impact of previous bone/joint conditions. This review provides an update on the use of conventional radionuclide imaging tests and recent advancements in fusion imaging scans for the differential diagnosis of musculoskeletal infections. Furthermore, it examines the role of radionuclide scans in monitoring treatment responses and explores current trends in their application. We anticipate that this update will be of significant interest to internists, rheumatologists, radiologists, orthopedic surgeons, rehabilitation physicians, and other specialists involved in musculoskeletal pathology.

The promise and limitations of artificial intelligence in musculoskeletal imaging

With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.

Differentiating Magnetic Resonance Images of Pyogenic Spondylitis and Spinal Modic Change Using a Convolutional Neural Network

STUDY DESIGN

A retrospective analysis of magnetic resonance imaging (MRI).

OBJECTIVE

The study aimed to evaluate the performance of a convolutional neural network (CNN) to differentiate spinal pyogenic spondylitis from Modic change on MRI. We compared the performance of CNN to that of four clinicians.

SUMMARY OF BACKGROUND DATA

Discrimination between pyogenic spondylitis and spinal Modic change is crucial in clinical practice. CNN deep-learning approaches for medical imaging are being increasingly utilized.

METHODS

We retrospectively reviewed MRIs from pyogenic spondylitis and spinal Modic change patients. There were 50 patients per group. Sagittal T1-weighted (T1WI), sagittal T2-weighted (T2WI), and short TI inversion recovery (STIR) MRIs were used for CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate CNN performance, we plotted the receiver operating characteristic curve and calculated the area under the curve. We compared the accuracy, sensitivity, and specificity of CNN diagnosis to that of a radiologist, spine surgeon, and two orthopedic surgeons.

RESULTS

The CNN-based area under the curves of the receiver operating characteristic curve from the T1WI, T2WI, and STIR were 0.95, 0.94, and 0.95, respectively. The accuracy of the CNN was significantly greater than that of the four clinicians on T1WI and STIR (P<0.05), and better than a radiologist and one orthopedic surgeon on the T2WI (P<0.05). The sensitivity was significantly better than that of the four clincians on T1WI and STIR (P<0.05), and better than a radiologist and one orthopedic surgeon on the T2WI (P<0.05). The specificity was significantly better than one orthopedic surgeon on T1WI and T2WI (P<0.05) and better than both orthopedic surgeons on STIR (P<0.05).

CONCLUSION

We differentiated between Modic changes and pyogenic spondylitis using a CNN that interprets MRI. The performance of the CNN was comparable to, or better than, that of the four clinicians.

Convolutional Neural Networks in Spinal Magnetic Resonance Imaging: A Systematic Review

OBJECTIVE

Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CNNs in image segmentation and pathology detection in spine magnetic resonance imaging.

METHODS

For this systematic literature review, the authors performed a systematic initial search of the PubMed/Medline and Web of Science (Core collection) databases for eligible investigations. The authors limited the search to observational studies. Outcome parameters were analyzed according to the inclusion criteria and assigned to 3 groups: 1) segmentation of anatomical structures, 2) segmentation and evaluation of pathologic structures, and 3) specific implementation of CNNs.

RESULTS

Twenty-four retrospectively designed articles met the inclusion criteria. Publication dates ranged from 2017 to 2021. In total, 14,065 patients with 113,110 analyzed images were included. Most authors trained their network with a training-to-testing ratio of 80/20, while all but 2 articles used 5- to 10-fold cross-validation. Nine articles compared their performance results with other neural networks and algorithms, and all 24 articles described outcomes as positive.

CONCLUSIONS

State-of-the-art CNNs can detect and segment-specific anatomical landmarks and pathologies across a wide range, comparable to the skills of radiologists and experienced clinicians. With rapidly evolving network architectures and growing medical image databases, the future is likely to show growth in the development and refinement of these capable networks. However, the aid of automated segmentation and classification by neural networks cannot and should not be expected to replace clinical experts.

Current Applications of Machine Learning in Spine: From Clinical View

STUDY DESIGN

Narrative review.

OBJECTIVES

This review aims to present current applications of machine learning (ML) in spine domain to clinicians.

METHODS

We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.

RESULTS

Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.

CONCLUSIONS

ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Imaging of Spine Infections

The incidence of spondylodiskitis has increased over the last 20 years worldwide, especially in the immunodepressed population, and it remains a complex pathology, both in terms of diagnosis and treatment. Because clinical symptoms are often nonspecific and blood culture negative, imaging plays an essential role in the diagnostic process. Magnetic resonance imaging, in particular, is the gold standard technique because it can show essential findings such as vertebral bone marrow, disk signal alteration, a paravertebral or epidural abscess, and, in the advanced stage of disease, fusion or collapse of the vertebral elements. However, many noninfectious spine diseases can simulate spinal infection. In this article, we present imaging features of specific infectious spine diseases that help radiologists make the distinction between infectious and noninfectious processes.

Current development and prospects of deep learning in spine image analysis: a literature review

BACKGROUND AND OBJECTIVE

As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature.

METHODS

A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed.

KEY CONTENT AND FINDINGS

The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability.

CONCLUSIONS

The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future.

Comparisons of Glutamate in the Brains of Alzheimer’s Disease Mice Under Chemical Exchange Saturation Transfer Imaging Based on Machine Learning Analysis

Chemical exchange saturation transfer (CEST) is one of the molecular magnetic resonance imaging (MRI) techniques that indirectly measures low-concentration metabolite or free protein signals that are difficult to detect by conventional MRI techniques. We applied CEST to Alzheimer’s disease (AD) and analyzed both region of interest (ROI) and pixel dimensions. Through the analysis of the ROI dimension, we found that the content of glutamate in the brains of AD mice was higher than that of normal mice of the same age. In the pixel-dimensional analysis, we obtained a map of the distribution of glutamate in the mouse brain. According to the experimental data of this study, we designed an algorithm framework based on data migration and used Resnet neural network to classify the glutamate distribution images of AD mice, with an accuracy rate of 75.6%. We evaluate the possibility of glutamate imaging as a biomarker for AD detection for the first time, with important implications for the detection and treatment of AD.

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.

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

AI MSK clinical applications: spine imaging

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images

BACKGROUND CONTEXT

Accurate diagnosis of osteoporotic vertebral fracture (OVF) is important for improving treatment outcomes; however, the gold standard has not been established yet. A deep-learning approach based on convolutional neural network (CNN) has attracted attention in the medical imaging field.

PURPOSE

To construct a CNN to detect fresh OVF on magnetic resonance (MR) images.

STUDY DESIGN/SETTING

Retrospective analysis of MR images PATIENT SAMPLE: This retrospective study included 814 patients with fresh OVF. For CNN training and validation, 1624 slices of T1-weighted MR image were obtained and used.

OUTCOME MEASURE

We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) in order to evaluate the performance of the CNN. Consequently, the sensitivity, specificity, and accuracy of the diagnosis by CNN and that of the two spine surgeons were compared.

METHODS

We constructed an optimal model using ensemble method by combining nine types of CNNs to detect fresh OVFs. Furthermore, two spine surgeons independently evaluated 100 vertebrae, which were randomly extracted from test data.

RESULTS

The ensemble method using VGG16, VGG19, DenseNet201, and ResNet50 was the combination with the highest AUC of ROC curves. The AUC was 0.949. The evaluation metrics of the diagnosis (CNN/surgeon 1/surgeon 2) for 100 vertebrae were as follows: sensitivity: 88.1%/88.1%/100%; specificity: 87.9%/86.2%/65.5%; accuracy: 88.0%/87.0%/80.0%.

CONCLUSIONS

In detecting fresh OVF using MR images, the performance of the CNN was comparable to that of two spine surgeons.

Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents

INTRODUCTION

Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent.

METHODS

We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM-RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation.

CONCLUSIONS

The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.

Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI

BACKGROUND

Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources.

PURPOSE

To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE.

STUDY TYPE

Retrospective.

POPULATION

One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation.

FIELD STRENGTH/SEQUENCE

3.0 T; fast spin-echo (T₁ WI, T₂ WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging.

ASSESSMENT

Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists.

STATISTICAL TESTS

The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant).

RESULTS

In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%).

DATA CONCLUSION

The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN’s diagnostic accuracy and consistency using confusion matrices and McNemar’s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine.

Assessment of diffusion tensor imaging in differentiation between pyogenic and tuberculous spondylitis

PURPOSE

to assess diffusion tensor imaging (DTI); an emerging technique for differentiation between pyogenic and tuberculous spondylitis.

PATIENTS AND METHODS

The study was carried out on 33 patients with infective spondylitis performing conventional MRI and DTI. The mean diffusivity (MD) and fractional anisotropy (FA) of the affected vertebral body were calculated by two readers.

RESULTS

The MD of pyogenic spondylitis of both readers (1.48 ± 0.09 and 1.47 ± 0.08 × 10^-3 mm²/s) were significantly higher values (P = 0.001) than tuberculous spondylitis (1.11 ± 0.15 and 1.18 ± 0.08 × 10^-3 mm²/s). The FA of pyogenic spondylitis of both readers (0.18 ± 0.09 and 0.20 ± 0.08) were significantly lower values (P = 0.001) than tuberculous spondylitis (0.30 ± 0.05 and 0.32 ± 0.03). There was a strong inter-reader agreement between both readers using MD (K = 0.963) and FA (K = 0.858). The thresholds MD and FA used for differentiating pyogenic and tuberculous spondylitis of both readers were 1.37 and 1.33 × 10^-3 mm²/s and 0.21 and 0.25 with the area under the curve (AUC) of 0.927 and 0.831 respectively. Combined MD and FA revealed increased AUC to 0.97 and 0.98 of both readers respectively.

CONCLUSION

DTI with its parameters can be considered a noninvasive beneficial quantitative method that can help in differentiation between pyogenic and tuberculous spondylitis.

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1607-1619.

Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review

BACKGROUND

Machine learning (ML) is a subdomain of artificial intelligence that enables computers to abstract patterns from data without explicit programming. A myriad of impactful ML applications already exists in orthopaedics ranging from predicting infections after surgery to diagnostic imaging. However, no systematic reviews that we know of have compared, in particular, the performance of ML models with that of clinicians in musculoskeletal imaging to provide an up-to-date summary regarding the extent of applying ML to imaging diagnoses. By doing so, this review delves into where current ML developments stand in aiding orthopaedists in assessing musculoskeletal images.

QUESTIONS/PURPOSES

This systematic review aimed (1) to compare performance of ML models versus clinicians in detecting, differentiating, or classifying orthopaedic abnormalities on imaging by (A) accuracy, sensitivity, and specificity, (B) input features (for example, plain radiographs, MRI scans, ultrasound), (C) clinician specialties, and (2) to compare the performance of clinician-aided versus unaided ML models.

METHODS

A systematic review was performed in PubMed, Embase, and the Cochrane Library for studies published up to October 1, 2019, using synonyms for machine learning and all potential orthopaedic specialties. We included all studies that compared ML models head-to-head against clinicians in the binary detection of abnormalities in musculoskeletal images. After screening 6531 studies, we ultimately included 12 studies. We conducted quality assessment using the Methodological Index for Non-randomized Studies (MINORS) checklist. All 12 studies were of comparable quality, and they all clearly included six of the eight critical appraisal items (study aim, input feature, ground truth, ML versus human comparison, performance metric, and ML model description). This justified summarizing the findings in a quantitative form by calculating the median absolute improvement of the ML models compared with clinicians for the following metrics of performance: accuracy, sensitivity, and specificity.

RESULTS

ML models provided, in aggregate, only very slight improvements in diagnostic accuracy and sensitivity compared with clinicians working alone and were on par in specificity (3% (interquartile range [IQR] -2.0% to 7.5%), 0.06% (IQR -0.03 to 0.14), and 0.00 (IQR -0.048 to 0.048), respectively). Inputs used by the ML models were plain radiographs (n = 8), MRI scans (n = 3), and ultrasound examinations (n = 1). Overall, ML models outperformed clinicians more when interpreting plain radiographs than when interpreting MRIs (17 of 34 and 3 of 16 performance comparisons, respectively). Orthopaedists and radiologists performed similarly to ML models, while ML models mostly outperformed other clinicians (outperformance in 7 of 19, 7 of 23, and 6 of 10 performance comparisons, respectively). Two studies evaluated the performance of clinicians aided and unaided by ML models; both demonstrated considerable improvements in ML-aided clinician performance by reporting a 47% decrease of misinterpretation rate (95% confidence interval [CI] 37 to 54; p < 0.001) and a mean increase in specificity of 0.048 (95% CI 0.029 to 0.068; p < 0.001) in detecting abnormalities on musculoskeletal images.

CONCLUSIONS

At present, ML models have comparable performance to clinicians in assessing musculoskeletal images. ML models may enhance the performance of clinicians as a technical supplement rather than as a replacement for clinical intelligence. Future ML-related studies should emphasize how ML models can complement clinicians, instead of determining the overall superiority of one versus the other. This can be accomplished by improving transparent reporting, diminishing bias, determining the feasibility of implantation in the clinical setting, and appropriately tempering conclusions.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Artificial Intelligence in Health Care: Current Applications and Issues

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.

A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma

STUDY DESIGN

Retrospective analysis of magnetic resonance imaging (MRI).

OBJECTIVE

The aim of this study was to evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI. We compared the performance of the CNN and that of two expert radiologists.

SUMMARY OF BACKGROUND DATA

Preoperative discrimination between spinal schwannomas and meningiomas is crucial because different surgical procedures are required for their treatment. A deep-learning approach based on CNNs is gaining interest in the medical imaging field.

METHODS

We retrospectively reviewed data from patients with spinal schwannoma and meningioma who had undergone MRI and tumor resection. There were 50 patients with schwannoma and 34 patients with meningioma. Sagittal T2-weighted magnetic resonance imaging (T2WI) and sagittal contrast-enhanced T1-weighted magnetic resonance imaging (T1WI) were used for the CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate the performance of the CNN, we plotted the receiver-operating characteristic (ROC) curve and calculated the area under the curve (AUC). We calculated and compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN and two board-certified radiologists.

RESULTS

. The AUC of ROC curves of the CNN based on T2WI and contrast-enhanced T1WI were 0.876 and 0.870, respectively. The sensitivity of the CNN based on T2WI was 78%; 100% for radiologist 1; and 95% for radiologist 2. The specificity was 82%, 26%, and 42%, respectively. The accuracy was 80%, 69%, and 73%, respectively. By contrast, the sensitivity of the CNN based on contrast-enhanced T1WI was 85%; 100% for radiologist 1; and 96% for radiologist 2. The specificity was 75%, 56, and 58%, respectively. The accuracy was 81%, 82%, and 81%, respectively.

CONCLUSION

We have successfully differentiated spinal schwannomas and meningiomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists.

LEVEL OF EVIDENCE

4.

Learning physical properties of liquid crystals with deep convolutional neural networks

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy. We show that these deep neural networks identify liquid crystal phases and predict the order parameter of simulated nematic liquid crystals almost perfectly. We also show that convolutional neural networks identify the pitch length of simulated samples of cholesteric liquid crystals and the sample temperature of an experimental liquid crystal with very high precision.

A Review on the Use of Artificial Intelligence in Spinal Diseases

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.

An Application of Artificial Intelligence to Diagnostic Imaging of Spine Disease: Estimating Spinal Alignment From Moiré Images

The use of artificial intelligence (AI) as a tool supporting the diagnosis and treatment of spinal diseases is eagerly anticipated. In the field of diagnostic imaging, the possible application of AI includes diagnostic support for diseases requiring highly specialized expertise, such as trauma in children, scoliosis, symptomatic diseases, and spinal cord tumors. Moiré topography, which describes the 3-dimensional surface of the trunk with band patterns, has been used to screen students for scoliosis, but the interpretation of the band patterns can be ambiguous. Thus, we created a scoliosis screening system that estimates spinal alignment, the Cobb angle, and vertebral rotation from moiré images. In our system, a convolutional neural network (CNN) estimates the positions of 12 thoracic and 5 lumbar vertebrae, 17 spinous processes, and the vertebral rotation angle of each vertebra. We used this information to estimate the Cobb angle. The mean absolute error (MAE) of the estimated vertebral positions was 3.6 pixels (~5.4 mm) per person. T1 and L5 had smaller MAEs than the other levels. The MAE per person between the Cobb angle measured by doctors and the estimated Cobb angle was 3.42°. The MAE was 4.38° in normal spines, 3.13° in spines with a slight deformity, and 2.74° in spines with a mild to severe deformity. The MAE of the angle of vertebral rotation was 2.9°±1.4°, and was smaller when the deformity was milder. The proposed method of estimating the Cobb angle and AVR from moiré images using a CNN is expected to enhance the accuracy of scoliosis screening.

Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children

The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (≤5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, p = 0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, p = 0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.

Automated acquisition of explainable knowledge from unannotated histopathology images

Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.

Technologies for acquiring explainable features from medical images need further development. Here, the authors report a deep learning based automated acquisition of explainable features from pathology images, and show a higher accuracy of their method as compared to pathologist based diagnosis of prostate cancer recurrence.