Hou Yuxin, Fan Zhiping, Li Jiaqi, Zeng Zi, Lv Gang, Lin Jingsheng, Zhou Liang, Wu Tao, Cao Qing
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, China.
College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Can J Cardiol. 2025 Feb;41(2):278-290. doi: 10.1016/j.cjca.2024.09.018. Epub 2024 Sep 27.
Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. In this study we developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to identify patients with low ejection fraction and predict LVEF values.
The electrocardiogram data were used as input, and the algorithm generated the probability of the patient suffering a low ejection fraction and estimated the LVEF value. A 5-year follow-up study on a group of individuals who initially had normal LVEF values was also performed. Furthermore, external validation of the algorithm performance was conducted using the Medical Information Mart for Intensive Care-IV database.
The algorithm's performance on the test set yielded an area under the curve value of 0.965 for detecting LVEF ≤ 50%. The algorithm had an accuracy of 92.8%, sensitivity of 88.8%, and specificity of 92.9%. For LVEF regression, the method showed a mean absolute error of 5.28 (95% confidence interval, 5.23-5.33) for the testing set. Additionally, the algorithm obtained an area under the curve value of 0.848 and a mean absolute error value of 9.56 during external validation. Patients with false positive results had a significantly greater likelihood of developing a low ejection fraction compared with patients who received true negative results (26.2% vs 2.0%; P < 0.0001).
The AI-ECG algorithm is capable of identifying low ejection fraction in patients with high accuracy. The AI-ECG algorithm is an efficient, prompt, and cost-effective screening tool for early heart failure.
左心室射血分数（LVEF）降低引发心力衰竭，迅速识别低射血分数对于控制病情进展和避免死亡至关重要。在本研究中，我们开发了一种基于人工智能的心电图（AI-ECG）算法，以识别低射血分数患者并预测LVEF值。
将心电图数据用作输入，该算法生成患者发生低射血分数的概率并估算LVEF值。我们还对一组初始LVEF值正常的个体进行了为期5年的随访研究。此外，使用重症监护医学信息数据库IV对该算法的性能进行了外部验证。
该算法在测试集上检测LVEF≤50%的曲线下面积值为0.965。该算法的准确率为92.8%，灵敏度为88.8%，特异性为92.9%。对于LVEF回归，该方法在测试集上的平均绝对误差为5.28（95%置信区间，5.23 - 5.33）。此外，该算法在外部验证期间的曲线下面积值为0.848，平均绝对误差值为9.56。与获得真阴性结果的患者相比，假阳性结果的患者发生低射血分数的可能性显著更高（26.2%对2.0%；P < 0.0001）。
AI-ECG算法能够高精度地识别低射血分数患者。AI-ECG算法是一种用于早期心力衰竭的高效、快速且具有成本效益的筛查工具。
