Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist

The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks

We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process utilized the EfficientNetV2-M network. The model's ability to generalize was assessed by conducting a rigorous evaluation on an entirely independent test set and comparing its performance with the diagnoses made by three orthopedists and three radiologists. The evaluation metrics employed to assess the model's performance included accuracy, sensitivity, specificity, and F1 score. Additionally, the weight distribution of the network was visualized using gradient-weighted class activation mapping (Grad-CAM). For the doctor group, accuracy ranged from 87.9 to 90.0% (mean, 89.0%), precision ranged from 87.2 to 90.5% (mean, 89.0%), sensitivity ranged from 87.1 to 91.0% (mean, 89.2%), specificity ranged from 93.7 to 94.7% (mean, 94.3%), and F1 score ranged from 88.2 to 89.9% (mean, 89.1%). The DCNN model had accuracy of 92.0%, precision of 91.9%, sensitivity of 92.2%, specificity of 95.7%, and F1 score of 92.0%. Grad-CAM exhibited concentrations of highlighted areas in the intervertebral foraminal region. We developed a DCNN model that intelligently distinguished spondylolysis or spondylolisthesis on lumbar lateral or lumbar dynamic radiographs.

The Accuracy of Artificial Intelligence Models in Hand/Wrist Fracture and Dislocation Diagnosis: A Systematic Review and Meta-Analysis

BACKGROUND

Early and accurate diagnosis is critical to preserve function and reduce healthcare costs in patients with hand and wrist injury. As such, artificial intelligence (AI) models have been developed for the purpose of diagnosing fractures through imaging. The purpose of this systematic review and meta-analysis was to determine the accuracy of AI models in identifying hand and wrist fractures and dislocations.

METHODS

Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Diagnostic Test Accuracy guidelines, Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched from their inception to October 10, 2023. Studies were included if they utilized an AI model (index test) for detecting hand and wrist fractures and dislocations in pediatric (<18 years) or adult (>18 years) patients through any radiologic imaging, with the reference standard established through image review by a medical expert. Results were synthesized through bivariate analysis. Risk of bias was assessed using the QUADAS-2 tool. This study was registered with PROSPERO (CRD42023486475). Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation.

RESULTS

A systematic review identified 36 studies. Most studies assessed wrist fractures (27.90%) through radiograph imaging (94.44%), with radiologists serving as the reference standard (66.67%). AI models demonstrated area under the curve (0.946), positive likelihood ratio (7.690; 95% confidence interval, 6.400-9.190), and negative likelihood ratio (0.112; 0.0848-0.145) in diagnosing hand and wrist fractures and dislocations. Examining only studies characterized by a low risk of bias, sensitivity analysis did not reveal any difference from the overall results. Overall certainty of evidence was moderate.

CONCLUSION

In demonstrating the accuracy of AI models in hand and wrist fracture and dislocation diagnosis, we have demonstrated that the potential use of AI in diagnosing hand and wrist fractures is promising.

LEVEL OF EVIDENCE

Level III. See Instructions for Authors for a complete description of levels of evidence.

Accuracy of wrist fracture detection on radiographs by artificial intelligence compared to human clinicians. A systematic review and meta-analysis

PURPOSE

The aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures.

METHOD

This study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively.

RESULTS

Of 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88-0.95) and 0.89 (0.84-0.92), respectively. The corresponding values for human readers were 0.95 (0.91-0.97) and 0.94 (0.91-0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73-0.92) and 0.83 (0.76-0.89), while human experts exhibited 0.71 (0.66-0.76) and 0.93 (0.90-0.95), respectively.

CONCLUSION

The results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.

Easily missed pathologies of the musculoskeletal system in the emergency radiology setting

The musculoskeletal region is the main area in terms of easily missed pathologies in the emergency radiology setting, because the majority of diagnoses missed in the emergency setting are fractures.A review of the literature was performed by searching the PubMed and ScienceDirect databases, using the keywords ('missed injuries' or 'missed fractures') and ('emergency radiology' or 'emergency room') and ('musculoskeletal' or 'bone' or 'skeleton') for the title and abstract query. The inclusion criteria were scientific papers presented in the English and German languages. Among the 347 relevant hits between 1980 and 2024 as identified by the author of this review article, there were 114 relevant articles from the years between 2018 and 2024. Based on this literature search and the author's personal experience, this study presents useful information for reducing the number of missed pathologies in the musculoskeletal system in the emergency radiology setting.Predominant factors that make up the majority of missed fractures are 'subtle but still visible fractures' and 'radiographically imperceptible fractures'. Radiologists are able to minimize the factors contributing to fractures being missed. For example, implementing a 'four-eyes principle', i.e., two readers read the radiographs, would help to overcome the missing of 'subtle but still visible fractures' and the additional use of cross-sectional imaging would help to overcome the missing of 'radiographically imperceptible fractures'. Knowledge of what is commonly missed and evaluation of high-risk areas with utmost care also increase the diagnostic performance of radiologists. · Radiological imaging in an emergency setting increases the likelihood of radiological diagnostic errors, such as missing musculoskeletal pathologies.. · The majority of diagnoses missed in the emergency setting are fractures.. · To lessen the number of easily missed pathologies in the musculoskeletal system in the emergency radiology setting, a systematic approach is necessary.. · Adequate training of radiologists in emergency radiology and close collaboration with clinical partners are important measures to decrease the number of missed musculoskeletal injuries.. · Weber MA. Easily missed pathologies of the musculoskeletal system in the emergency radiology setting. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8330.

Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis

PURPOSE

Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it.

METHODS

We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression.

RESULTS

Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction.

CONCLUSIONS

Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.

Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis

OBJECTIVES

Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness.

MATERIALS AND METHODS

This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve.

RESULTS

Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies.

CONCLUSIONS

The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting.

CLINICAL RELEVANCE STATEMENT

Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries.

KEY POINTS

• Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.

Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition

Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review

PURPOSE

To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts.

METHODS

PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded.

RESULTS

A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans.

CONCLUSIONS

AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance.

CLINICAL RELEVANCE

AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures

Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field.

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy

PURPOSE

Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation.

MATERIALS AND METHODS

A pre-trained DL algorithm was specifically fine-tuned on static and dynamic dorsopalmar wrist radiography (training data set n = 201) for the automated assessment of the SL distance. Afterwards the DL algorithm was evaluated (evaluation data set n = 364 patients with n = 1604 radiographs) and correlated with results of an experienced human reader and with arthroscopic findings.

RESULTS

The evaluation data set comprised arthroscopically diagnosed SL insufficiency according to Geissler's stages 0-4 (56.5%, 2.5%, 5.5%, 7.5%, 28.0%). Diagnostic accuracy of the DL algorithm on dorsopalmar radiography regarding SL integrity was close to that of the human reader (e.g. differentiation of Geissler's stages ≤ 2 versus > 2 with a sensitivity of 74% and a specificity of 78% compared to 77% and 80%) with a correlation coefficient of 0.81 (P < 0.01).

CONCLUSION

A DL algorithm like this might become a valuable tool supporting clinicians' initial decision making on radiography regarding SL integrity and consequential triage for further patient management.

Application of Artificial Intelligence and Machine Learning in Diagnosing Scaphoid Fractures: A Systematic Review

The integration of artificial intelligence (AI) in healthcare has sparked interest in its potential to revolutionize medical diagnostics. This systematic review explores the application of AI and machine learning (ML) techniques in diagnosing scaphoid fractures, which account for a significant percentage of carpal bone fractures and have important implications for wrist function. Scaphoid fractures, common in young and active individuals, require an early and accurate diagnosis for effective treatment. AI has the potential to automate and improve the accuracy of scaphoid fracture detection on radiography, aiding in early diagnosis and reducing unnecessary clinical examinations. This systematic review discusses the methods used to identify relevant studies, including search criteria and quality assessment tools, and presents the results of the selected studies. The findings indicate that AI-driven methods can improve diagnostic accuracy, reducing the risk of missed fractures and complications. AI assistance can also alleviate the workload of medical professionals, improving diagnostic efficiency and reducing observer fatigue. However, challenges such as algorithm limitations and the need for continuous refinement must be addressed to ensure reliable fracture identification. This review underscores the clinical significance of AI-assisted diagnostics, especially in cases where fractures may be subtle or occult. It emphasizes the importance of integrating AI into medical education and training and calls for robust data collection and collaboration between AI developers and medical practitioners. Future research should focus on larger datasets, algorithm improvement, cost-effectiveness assessment, and international partnerships to fully harness the potential of AI in diagnosing scaphoid fractures.

Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions

In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions.

Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists

The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.

Clinical Validation of an Artificial Intelligence Model for Detecting Distal Radius, Ulnar Styloid, and Scaphoid Fractures on Conventional Wrist Radiographs

This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887-0.918), 0.925 (95% C.I. 0.911-0.939), and 0.808 (95% C.I. 0.748-0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66-0.83) to 0.85 (95% C.I. 0.77-0.93) and from 0.71 (95% C.I. 0.62-0.80) to 0.80 (95% C.I. 0.71-0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed.