Department of Cardiology, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China.
Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, Zhejiang, China.
BMJ Open. 2024 Mar 29;14(3):e074288. doi: 10.1136/bmjopen-2023-074288.
Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial.
In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups.
The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals.
ChiCTR2300069496.
二尖瓣反流（MR）是最常见的瓣膜性心脏病，发病率为 2.5%。虽然超声心动图常用于评估 MR，但它有许多局限性，特别是对于大规模的 MR 筛查。心脏听诊与电子听诊器和人工智能（AI）相结合，可以成为评估 MR 严重程度的快速且经济的方式。我们的目标是（1）建立一种基于深度神经网络（DNN）的心脏听诊方法来评估 MR 的严重程度；（2）通过虚拟临床试验定量衡量开发的基于 AI 的 MR 评估方法的性能。
在一项横断面设计中，将在心尖区记录心音图。招募的患者将通过超声心动图检查以确认 MR 或无 MR 的诊断。超声心动图参数将作为评估 MR 严重程度的金标准，分为四级：无、轻度、中度和重度。该研究分为两个阶段。首先，将创建一个与 MR 相关的心脏声音数据库，在该数据库上训练基于 DNN 的 MR 严重程度分类器。自动 MR 严重程度分类器将与 Smartho-D2 电子听诊器集成。其次，将在独立的临床验证数据集评估开发的智能设备的性能。将评估开发的智能 MR 评估设备的灵敏度、特异性、精度、准确性和 F1 分数。将检查心脏病专家用户和患者用户对智能设备性能的一致性。还将通过对四个严重程度组的闭塞图引导变量进行统计比较来研究开发模型的可解释性。
本研究方案已获得中国湖州市中心医院医学伦理委员会的批准（注册号：202302009-01）。所有参与者均需签署知情同意书。传播将通过会议演讲和同行评议期刊进行。
ChiCTR2300069496。
