Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI)

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.

Ultrasound for Automated Classification of Full-Thickness Rotator Cuff Tendon Tears using Deep Learning

Rotator cuff tendon tears, the most common shoulder injuries, are typically diagnosed mainly through MRI, but can also be seen on ultrasound (US), a much less costly test that currently requires highly-trained human expert operators. An AI tool to identify full-thickness rotator cuff tears in US could make this test much more accessible in clinical practice. We propose a two-step approach starting with segmentation and followed by classification. Automatic segmentation of US scans is challenging due to speckle noise and low contrast. We utilized a CNN-autoencoder that predicts boundary contour points of humeral cortex and subacromial bursa directly from raw US images rather than the popular pixel-wise semantic segmentation. Then both the original US image and the corresponding segmentation mask are passed to a classification network (VGG-16) to determine whether tendons are torn or intact. This novel approach only passes the key portions of the scan (in which any tears are most visible) to the classification network, maximizing detection accuracy and clinical relevance. We evaluated this approach on data prospectively acquired from 210 patients, training with 11,600 images and testing with 2900 images. We had an average segmentation Dice coefficient (DC) of 95.3% and Hausdorff Distance (HD) of 2.9 mm, outperforming a U-Net model (DC=90.5%, HD=6.8 mm). The classification network, VGG-16, achieved 85.2% accuracy (sensitivity 84.2%, specificity 83.3%) in classifying supraspinatus tendons as intact or torn from US images. Results indicate that our AI-driven US evaluation pipeline has the potential to enable less-experienced ultrasound users to detect rotator cuff tears with high accuracy and explainability. This can allow more healthcare professionals to conduct scans, improving timely patient access to imaging and streamlining treatment decisions.

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.

Artificial Intelligence and Machine Learning in Rotator Cuff Tears

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.