Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head-to-Head Comparison With Hospital-Based Physician ECG Readers.

BACKGROUND: Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high-risk patients is important but labor-intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges. METHODS AND RESULTS: We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI-based tool for subsequent AF detection using ECG records alone. A total of 5 million 30-second epochs from 329 patients were annotated as AF or non-AF by expert ECG readers for AI training and validation, while 5 million 30-second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively. CONCLUSIONS: Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Portable Device Improves the Detection of Atrial Fibrillation After Ablation.

Asymptomatic recurrences of atrial fibrillation (AF) have been found to be common after ablation.A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). The ECGs collected via BigThumb were compared using the automated AF detection algorithm, artificial intelligence (AI) algorithm, and cardiologists' manual review. Subsequent changes in adherence to oral anticoagulation of patients were also recorded. In this study, we examined 218 patients (109 in each group). After a follow-up of 345.4 ± 60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P = 0.0163), with more adherence to oral anticoagulation in BT Group (P = 0.0052). The participants in the BT Group recorded 26133 ECGs, among which 3299 (12.6%) were diagnosed as AF by cardiologists' manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%).As per our findings, it was determined that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence to oral anticoagulation. AI algorithm improves the accuracy of ECG diagnosis and has the potential to reduce the manual review.