Udompap Prowpanga, Liu Kan, Attia Itzhak Zachi, Canning Rachel E, Benson Joanne T, Therneau Terry M, Noseworthy Peter A, Friedman Paul A, Rattan Puru, Ahn Joseph C, Simonetto Douglas A, Shah Vijay H, Kamath Patrick S, Allen Alina M
Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Clin Gastroenterol Hepatol. 2025 Mar;23(4):574-582.e3. doi: 10.1016/j.cgh.2024.08.009. Epub 2024 Aug 27.
Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishing the presence of MASLD using 12-lead electrocardiogram (ECG).
This is a retrospective study of adults diagnosed with MASLD in Olmsted County, Minnesota, between 1996 and 2019. Both cases and controls had ECGs performed within 6 years before and 1 year after study entry. An AI-based ECG model using a convolutional neural network was trained, validated, and tested in 70%, 10%, and 20% of the cohort, respectively. External validation was performed in an independent cohort from Mayo Clinic Enterprise. The primary outcome was the performance of ECG to identify MASLD, alone or when added to clinical parameters.
A total of 3468 MASLD cases and 25,407 controls were identified. The AI-ECG model predicted the presence of MASLD with an area under the curve (AUC) of 0.69 (original cohort) and 0.62 (validation cohort). The performance was similar or superior to age- and sex-adjusted models using body mass index (AUC, 0.71), presence of diabetes, hypertension or hyperlipidemia (AUC, 0.68), or diabetes alone (AUC, 0.66). The model combining ECG, age, sex, body mass index, diabetes, and alanine aminotransferase had the highest AUC: 0.76 (original) and 0.72 (validation).
This is a proof-of-concept study that an AI-based ECG model can detect MASLD with a comparable or superior performance as compared with the models using a single clinical parameter but not superior to the combination of clinical parameters. ECG can serve as another screening tool for MASLD in the nonhepatology space.
需要可用于代谢功能障碍相关脂肪性肝病（MASLD）的无创筛查工具。我们旨在探索基于深度学习的人工智能（AI）模型利用12导联心电图（ECG）鉴别MASLD存在情况的性能。
这是一项对1996年至2019年间在明尼苏达州奥尔姆斯特德县被诊断为MASLD的成年人进行的回顾性研究。病例组和对照组在研究入组前6年内及入组后1年内均进行了心电图检查。使用卷积神经网络的基于AI的心电图模型分别在队列的70%、10%和20%中进行训练、验证和测试。在梅奥诊所企业的一个独立队列中进行了外部验证。主要结局是心电图单独或与临床参数联合时识别MASLD的性能。
共识别出3468例MASLD病例和25407例对照。AI-ECG模型预测MASLD存在情况的曲线下面积（AUC）在原队列中为0.69，在验证队列中为0.62。该模型的性能与使用体重指数（AUC为0.71）、糖尿病、高血压或高脂血症存在情况（AUC为0.68）或仅糖尿病（AUC为0.66）的年龄和性别调整模型相似或更优。结合心电图、年龄、性别、体重指数、糖尿病和丙氨酸氨基转移酶的模型具有最高的AUC：原队列中为0.76，验证队列中为0.72。
这是一项概念验证研究，表明基于AI的心电图模型在检测MASLD方面与使用单一临床参数的模型相比具有相当或更优的性能，但不优于临床参数联合模型。心电图可作为非肝病领域MASLD的另一种筛查工具。
