2025-08-20 インペリアル・カレッジ・ロンドン (ICL)
Researchers have developed an AI tool that can help doctors predict who might develop a potentially fatal heart condition, just from an ECG.
<関連情報>
- https://www.imperial.ac.uk/news/267593/ai-enhanced-ecg-spot-patients-risk-dangerous/
- https://jamanetwork.com/journals/jamacardiology/article-abstract/2837611
人工知能強化型心電図による完全心ブロックリスクの層別化 Artificial Intelligence–Enhanced Electrocardiography for Complete Heart Block Risk Stratification
Arunashis Sau, PhD; Henry Zhang, BSc; Joseph Barker, MRes; et al
JAMA Cardiology Published:August 20, 2025
DOI:10.1001/jamacardio.2025.2522
Key Points
Question Can artificial intelligence–enhanced electrocardiography (AI-ECG) be used to identify individuals with risk of incident complete heart block (CHB)?
Findings This cohort study demonstrated that an AI-ECG risk model can predict the risk of incident CHB and is superior to traditional, guideline-based, ECG risk markers.
Meaning The AI-ECG model termed AIRE-CHB could be used to risk-stratify patients at risk of CHB to guide treatment decisions such as rhythm monitoring or empirical pacemaker implantation.
Abstract
Introduction Complete heart block (CHB) is a life-threatening condition that can lead to ventricular standstill, syncopal injury, and sudden cardiac death, and current electrocardiography (ECG)-based risk stratification (presence of bifascicular block) is crude and has limited performance. Artificial intelligence–enhanced electrocardiography (AI-ECG) has been shown to identify a broad spectrum of subclinical disease and may be useful for CHB.
Objective To develop an AI-ECG risk estimator for CHB (AIRE-CHB) to predict incident CHB.
Design, Setting, and Participants This cohort study was a development and external validation prognostic study conducted at Beth Israel Deaconess Medical Center and validated externally in the UK Biobank volunteer cohort.
Exposure Electrocardiogram.
Main Outcomes and Measures A new diagnosis of CHB more than 31 days after the ECG. AIRE-CHB uses a residual convolutional neural network architecture with a discrete-time survival loss function and was trained to predict incident CHB.
Results The Beth Israel Deaconess Medical Center cohort included 1 163 401 ECGs from 189 539 patients. AIRE-CHB predicted incident CHB with a C index of 0.836 (95% CI, 0.819-0.534) and area under the receiver operating characteristics curve (AUROC) for incident CHB within 1 year of 0.889 (95% CI, 0.863-0.916). In comparison, the presence of bifascicular block had an AUROC of 0.594 (95% CI, 0.567-0.620). Participants in the high-risk quartile had an adjusted hazard ratio (aHR) of 11.6 (95% CI, 7.62-17.7; P < .001) for development of incident CHB compared with the low-risk group. In the UKB UK Biobank cohort of 50 641 ECGs from 189 539 patients, the C index for incident CHB prediction was 0.936 (95% CI, 0.900-0.972) and aHR, 7.17 (95% CI, 1.67-30.81; P < .001).
Conclusions and Relevance In this study, a first-of-its-kind deep learning model identified the risk of incident CHB. AIRE-CHB could be used in diverse settings to aid in decision-making for individuals with syncope or at risk of high-grade atrioventricular block.
