Nagao Azusa, Inagaki Yusuke, Nogami Keiji, Yamasaki Naoya, Iwasaki Fuminori, Liu Yang, Murakami Yoichi, Ito Takahiro, Takedani Hideyuki
Department of Blood Coagulation, Ogikubo Hospital, Tokyo, Japan.
Department of Rehabilitation Medicine, Nara Medical University, Nara, Japan.
Res Pract Thromb Haemost. 2024 May 9;8(4):102439. doi: 10.1016/j.rpth.2024.102439. eCollection 2024 May.
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.
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.
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.
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.
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.
关节出血可导致血友病患者出现滑膜炎和关节病，降低生活质量。尽管早期诊断与改善治疗效果相关，但诊断性超声检查需要专业经验。人工智能（AI）算法可能有助于超声检查诊断。
本研究将研发并评估一种用于检测血友病患者是否存在关节积血和滑膜炎的AI算法的诊断准确性。
收集了2010年1月至2022年3月期间血友病患者的肘部、膝部和踝部超声图像。这些图像用于训练和测试AI模型，以评估是否存在关节积血和滑膜炎。主要终点是诊断关节积血和滑膜炎的诊断准确性曲线下面积。其他终点包括准确率、精确率、灵敏度和特异性。
在收集的5649张图像中，3435张用于分析。肘部、膝部和踝关节检测关节积血的曲线下面积≥0.87，检测滑膜炎的曲线下面积≥0.90。检测关节积血的准确率和精确率分别≥0.74和≥0.67，检测滑膜炎的准确率和精确率分别≥0.83和≥0.74。对10至60岁血友病患者的分析显示结果一致。
AI模型有潜力辅助诊断并实现早期治疗干预，帮助血友病患者过上健康积极的生活。尽管AI模型在诊断方面显示出潜力，但关于异常发现所需的对照证据尚不清楚。长期观察对于评估对关节健康的影响至关重要。
