美国梅奥医学中心Paul A Friedman课题组取得一项新突破。他们研制了一种人工智能心电图算法，用于识别窦性心律期房颤患者。 该研究2019年9月7日发表在《柳叶刀》上。

房颤通常无症状，因此检测不足，但与中风、心力衰竭和死亡密切相关。现有的筛选方法需要长时间监测，且昂贵而低效。

课题组人员研发了一种人工智能心电图仪（AI-ECG），使用卷积神经网络探测正常窦性心律心房纤颤的心电图特征，旨在开发出一种快速、廉价、即时、机器学习的检测方法用于鉴别心房纤颤患者。

1993年12月31日至2017年7月21日，该课题组共纳入180922例患者的649931个正常窦性心律心电图。这些患者年龄在18岁及以上，均在梅奥医学中心心电图实验室至少进行一次仰卧位的正常窦性心律、标准10秒、12导联心电图。房颤阳性定义为至少有一个心电图出现心房颤动或心房扑动。

最终单独AI-ECG鉴别心房纤颤的AUC为0.87，灵敏度为79.0％，特异性为79.5%，F1得分为39.2％，整体准确率为79.4％。试验开始时，或首次诊断出心房纤颤的前一个月，包括AI-ECG在内的所有心电图结果鉴别心房纤颤的AUC为0.90，灵敏度为82.3%，特异性为83.4%，F1得分为45.4%，整体准确率为83.3%。

因此，AI-ECG有助于在护理时识别正常窦性心律期间心房纤颤的患者。

附：英文原文

Title: An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Author: Zachi I Attia, Peter A Noseworthy, Francisco Lopez-Jimenez, Samuel J Asirvatham, Abhishek J Deshmukh, Bernard J Gersh, Rickey E Carter, Xiaoxi Yao, Alejandro A Rabinstein, Brad J Erickson, Suraj Kapa, Paul A Friedman

Issue&Volume: Volume 394 Number 10201

Abstract: 

Background

Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.

Methods

We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs.

Findings

We included 180?922 patients with 649?931 normal sinus rhythm ECGs for analysis: 454?789 ECGs recorded from 126?526 patients in the training dataset, 64?340 ECGs from 18?116 patients in the internal validation dataset, and 130?802 ECGs from 36?280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86–0·88), sensitivity of 79·0% (77·5–80·4), specificity of 79·5% (79·0–79·9), F1 score of 39·2% (38·1–40·3), and overall accuracy of 79·4% (79·0–79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90–0·91), sensitivity to 82·3% (80·9–83·6), specificity to 83·4% (83·0–83·8), F1 score to 45·4% (44·2–46·5), and overall accuracy to 83·3% (83·0–83·7).

Interpretation

An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. 

DOI: 10.1016/S0140-6736(19)31721-0

Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(19)31721-0/fulltext