Wrzosek Michał, Buchwald Mikolaj, Czernik Patryk, Kupinski Szymon, Zatorska Karina, Jasińska Anna, Zakrzewski Dariusz, Pukacki Juliusz, Mazurek Cezary, Pękal Robert, Hryniewiecki Tomasz
Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.
Poznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
J Imaging Inform Med. 2025 Apr 21. doi: 10.1007/s10278-025-01497-4.
Diagnosis of aortic valve stenosis (AS) is performed manually by a physician experienced in echocardiography imaging. A specific subtype of AS, a severe low-gradient AS, is the most challenging one in terms of differentiating it from the moderate AS. In this study, an artificial intelligence (AI)-based model was used to diagnose the severe low-gradient AS in a fully automatic manner. Data from 158 consecutive patients undergoing echocardiography examination to assess AS severity were used. The obtained performance of our fully automatic approach was AUC = 0.719, 95% confidence interval, 0.640-0.798. It is an important step towards a comprehensive and automatic, image-only-based clinical decision support system for determining the presence of AS and its severity, especially in AS subtypes, such as low-gradient AS.
主动脉瓣狭窄（AS）的诊断由一位在超声心动图成像方面经验丰富的医生手动进行。AS的一种特定亚型，即严重低梯度AS，在将其与中度AS区分开来方面是最具挑战性的。在本研究中，使用了一种基于人工智能（AI）的模型以全自动方式诊断严重低梯度AS。使用了158例连续接受超声心动图检查以评估AS严重程度的患者的数据。我们全自动方法所获得的性能为AUC = 0.719，95%置信区间为0.640 - 0.798。这是朝着建立一个全面、自动、仅基于图像的临床决策支持系统迈出的重要一步，该系统用于确定AS的存在及其严重程度，特别是在AS亚型中，如低梯度AS。
