Deep Learning-Based Computer-Aided Diagnosis of Osteochondritis Dissecans of the Humeral Capitellum Using Ultrasound Images.

BACKGROUND: Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. METHODS: The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. RESULTS: Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. CONCLUSIONS: We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.

PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. Conservative treatment is the preferred treatment for managing OCD, and early intervention significantly influences the possibility of complete disease resolution. The purpose of this study is to develop a deep learning-based classification model in ultrasound images for computer-aided diagnosis. METHODS: This paper proposes a deep learning-based OCD classification method in ultrasound images. The proposed method first detects the humeral capitellum detection using YOLO and then estimates the OCD probability of the detected region probability using VGG16. We hypothesis that the performance will be improved by eliminating unnecessary regions. To validate the performance of the proposed method, it was applied to 158 subjects (OCD: 67, Normal: 91) using five-fold-cross-validation. RESULTS: The study demonstrated that the humeral capitellum detection achieved a mean average precision (mAP) of over 0.95, while OCD probability estimation achieved an average accuracy of 0.890, precision of 0.888, recall of 0.927, F1 score of 0.894, and an area under the curve (AUC) of 0.962. On the other hand, when the classification model was constructed for the entire image, accuracy, precision, recall, F1 score, and AUC were 0.806, 0.806, 0.932, 0.843, and 0.928, respectively. The findings suggest the high-performance potential of the proposed model for OCD classification in ultrasonic images. CONCLUSION: This paper introduces a deep learning-based OCD classification method. The experimental results emphasize the effectiveness of focusing on the humeral capitellum for OCD classification in ultrasound images. Future work should involve evaluating the effectiveness of employing the proposed method by physicians during medical check-ups for OCD.

Effect of quantitative partial valgus stress during baseball pitching on ball velocity and subjective pitch-effort.

BACKGROUND: Excessive elbow valgus stress can often cause pitching elbow injuries, and rehabilitation is usually required before an athlete can resume playing. However, there is a lack of information on the partial load rehabilitation of pitching elbow injuries caused by valgus extension overload based on elbow valgus stress. The purpose of this study was to clarify how quantitative partial elbow valgus stress while pitching affects ball velocity and subjective pitch-effort. METHODS: Forty-six male baseball pitchers participated in this study. Each player wore a wearable device on the elbow that collected their pitch parameters. Ball velocity was measured using a radar gun. Each elbow valgus stress was measured while each player was instructed to throw 5 fastballs at full effort. Then, based on the average stress of the 5 throws (100% partial valgus stress), the 75% and 50% stresses were calculated (75% and 50% partial valgus stress, respectively). Each pitcher continued to pitch until the number of pitches thrown at the targeted elbow stress reached 5. Each player was asked about their subjective pitch-effort after completing each type of partial valgus stress pitch. Outcomes were statistically evaluated using either a 1-way repeated measures analysis of variance or 2-way analysis of variance. RESULTS: The ball velocity was 72% (95% confidence interval [CI], 69%-75%) and 58% (95% CI, 55%-61%) during the 75% and 50% partial valgus stress, respectively (P < .001). Subjective pitch-effort was 41% (95% CI, 38%-44%) and 19% (95% CI, 16%-22%) while pitching at 75% and 50% partial valgus stress, respectively (P < .001). CONCLUSIONS: It may be desirable to instruct pitchers to throw at less than 20% subjective pitch-effort of the max if they want to pitch at 50% partial valgus stress. Elbow valgus stress might correlate with ball velocity at 75% partial valgus stress pitch. These results could enable clinicians and coaches to perform safer return-to-throwing programs and prevent excessive load on the elbow.

Hegemann's Disease Detected with a Traumatic Olecranon Fracture: A Case Report.

Case: We describe a case of Hegemann's disease in a 10-year-old boy practicing karate. The disease was discovered by chance when evaluating a traumatic olecranon fracture. Radiography showed not only olecranon fracture but also a shortening of the epiphysis of the humeral trochlea and a segmental lesion with sclerosis. The trochlea lesion was considered asymptomatic Hegemann's disease. After the olecranon healed conservatively, the patient resumed karate activities and underwent follow-up radiography. The trochlea lesion gradually normalized after 2 years without symptoms. Conclusions: Regardless of the initial alarming radiographic findings, the lesion gradually healed, and the patient was able to return to sports activities.