A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging

Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.

The angle of the lower portion of the posterior cruciate ligament assists in the diagnosis of partial anterior cruciate ligament tears

PURPOSE

To compare the difference of angle of the lower portion of the posterior cruciate ligament (PCL) measured via magnetic resonance imaging (MRI) in patients with and without partial anterior cruciate ligament (ACL) tears and to investigate the effectiveness of the angle of the lower portion of the PCL in diagnosing partial ACL tears.

METHODS

From January 2022 to December 2022, a cohort of consecutive patients presenting with ACL tears who underwent ACL reconstruction and patients with isolated meniscus tears undergoing arthroscopic surgery were enroled for this study. The angle of the inferior portion of the PCL comprises α and β angles, and the posterior offset of the lateral condyle were measured on the MRI and compared between the partial ACL tear and control groups. Receiver operating characteristic curves, the areas under the curve (AUCs) and the 95% confidence intervals (CIs) were calculated to identify cutoff values for diagnosing partial ACL injuries.

RESULTS

Following an assessment of cohort eligibility and matching for age and sex, 100 patients were included in this study. The mean age of the cohort was 46.1 ± 10.3 years. The AUC for the α angle was 0.88 (95% CI, 0.82-0.94), with a sensitivity of 0.74 and specificity of 0.84 for predicting partial ACL ruptures; the α angle cutoff value was 73.6° (diagnostic odds ratio (OR), 14.10; 95% CI, 5.33-37.28). The AUC for the β angle was 0.86 (95% CI, 0.79-0.93), with a sensitivity of 0.64 and a specificity of 0.92 for predicting partial ACL ruptures; the β angle cutoff value was 73.3° (diagnostic OR, 14.54; 95% CI, 5.76-36.68).

CONCLUSIONS

A small α angle and a large β angle were associated with partial ACL tears. The angle of the distal portion of the PCL was simple to measure and reproducible, enhancing the diagnosis of partial ACL tears.

LEVEL OF EVIDENCE

Level III.

Transformer-Based Multilabel Deep Learning Model Is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on Magnetic Resonance Imaging and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability

PURPOSE

To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance.

METHODS

DL models were developed and externally validated on retrospectively collected ankle magnetic resonance imaging (MRI) between April 2016 and March 2022 respectively at 3 centers. Included patients had confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multilabel paradigm. A transformer-based multilabel DL model (AnkleNet) was developed and compared with 4 convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC).

RESULTS

Our transformer-based model achieved AUCs of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which were significantly higher than those of CNN-based models (all P < .001). In terms of further CAI diagnosis, there was a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = .042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet.

CONCLUSIONS

Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially patients with RCAI.

CLINICAL RELEVANCE

Developing such an algorithm can improve the diagnostic performance of clinicians, aiding in identifying patients who would benefit from arthroscopy, such as patients with RCAI.

Segond Fractures Can Be Identified With Excellent Accuracy Utilizing Deep Learning on Anteroposterior Knee Radiographs

Purpose

To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts.

Methods

AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship-trained orthopaedic sports medicine surgeon with over 10 years of experience.

Results

A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set.

Conclusions

A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers.

Clinical Relevance

Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large-scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation

PURPOSE

To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues.

METHODS

A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment.

RESULTS

Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability.

CONCLUSIONS

DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework.

LEVEL OF EVIDENCE

Level IV, scoping review of Level I to IV studies.

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.