Abstract
BACKGROUND: Recurrent hemarthrosis and resultant hemophilic arthropathy are significant causes of morbidity in persons with hemophilia, despite the marked evolution of hemophilia care. Prevention, timely diagnosis, and treatment of bleeding episodes are key. However, a physical examination or a patient's assessment of musculoskeletal pain may not accurately identify a joint bleed. This difficulty is compounded as hemophilic arthropathy progresses. OBJECTIVES: Our system aims to utilize artificial intelligence and ultrasonography (US; point-of-care and handheld) to enable providers, and ultimately patients, to detect joint bleeds at the bedside and at home. We aimed to develop and assess the reliability of artificial intelligence algorithms in detecting and segmenting synovial recess distension (SRD; an indicator of disease activity) on US images of adult and pediatric knee, elbow, and ankle joints. METHODS: A total of 12,145 joint exams, comprising 61,501 US images from 7 international healthcare centers, were collected. The dataset included healthy participants and adult and pediatric persons with hemophilia, with and without SRD. Images were manually labeled by 2 experts and used to train binary convolutional neural network classifiers and segmentation models. Metrics to evaluate performance included accuracy, sensitivity, specificity, and area under the curve. RESULTS: The algorithms exhibited high performance across all joints and all cohorts. Specifically, the knee model showed an accuracy of 97%, sensitivity of 96%, specificity of 97%, and an area under the curve of 0.97 in SRD. High Dice coefficients (80%-85%) were achieved in segmentation tasks across all joints. CONCLUSION: This technology could assist with the early detection and management of hemarthrosis in hemophilia.