Artificial intelligence–assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection

Abstract

Background

Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses.

Objectives

This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia.

Methods

Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity.

Results

Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results.

Conclusion

AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.