基于人工智能的辅助诊断在先天性心脏病筛查中的应用。

Application of Artificial Intelligence-Based Auxiliary Diagnosis in Congenital Heart Disease Screening.

机构信息

Department of Cardiology, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, China;Fuwai Yunnan Cardiovascular Hospital, Kunming, China.

School of Information Science and Technology, Yunnan University, Kunming, China.

出版信息

Anatol J Cardiol. 2023 Apr;27(4):205-216. doi: 10.14744/AnatolJCardiol.2022.1386.

DOI:10.14744/AnatolJCardiol.2022.1386

PMID:36995059

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10098384/

Abstract

BACKGROUND

To evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease.

METHODS

From May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared.

RESULTS

Female sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032).

CONCLUSION

Artificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.

摘要

背景

评估基于人工智能的辅助诊断在先天性心脏病中的应用价值。

方法

2017 年 5 月至 2019 年 12 月，收集了 1892 例先天性心脏病心音进行学习和记忆辅助诊断。在 326 例先天性心脏病病例中验证了诊断率和分类识别。在 518 258 例先天性心脏病筛查中使用听诊和人工智能辅助诊断，并比较了先天性心脏病和肺动脉高压的检测准确率。

结果

与室间隔缺损/动脉导管未闭病例相比，房间隔缺损患者中女性和年龄>14 岁者居多（P<.001）。动脉导管未闭患者家族史更为突出（P<.001）。与无肺动脉高压相比，先天性心脏病-肺动脉高压患者中男性居多（P<.001），年龄与肺动脉高压显著相关（P=.008）。肺动脉高压组中发现了较高的心脏外畸形发生率。共有 326 例患者接受了人工智能检查。房间隔缺损的检出率为 73.8%，与听诊不同（P=.008）。室间隔缺损的检出率为 78.8%，动脉导管未闭的检出率为 88.9%。对 82 个乡镇和 1220 所学校的 518 258 人进行了筛查，包括 15 453 例疑似病例和 3930 例（7.58%）确诊病例。人工智能在室间隔缺损（P=.007）和动脉导管未闭（P=.021）分类中的检测准确率均高于听诊。对于正常病例，递归神经网络在先天性心脏病-肺动脉高压诊断中的准确率高达 97.77%（P=.032）。