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

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  Figure 1. Flow diagram of study design. A total of 1032 consecutive patients scheduled for surgery were screened for study eligibility. The ECG/electrogram recordings in a total of 659 patients were divided chronologically into 3 data sets: learning/training (263), learning/validation (66), and testing (330), with a ratio of 40%:10%:50%. EF indicates ejection fraction.

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  Figure 2. Examples of unipolar atrial signal synchronized with simultaneous single‐lead surface ECGs. Examples of continuous recording of surface ECG (top line in each section) and simultaneous atrial epicardial electrograms (second line in each section). A, The electrogram reveals both atrial fibrillation and corresponding ventricular activity (synchronous with QRSs from surface ECG). Atrial signals are more difficult to appreciate on the ECG alone, and given periods of relatively regular QRSs, the atrial fibrillation diagnosis may be missed when ECG readers are confronted with a high volume of recordings. The electrogram offered rhythm confirmation, thereby enhancing artificial intelligence (AI) learning capability. B, Both surface ECG (top line in each section) and atrial electrograms (second line in each section) are shown from a patient in whom the irregularity of the ECG suggests atrial fibrillation, whereas the discrete atrial electrograms at cycle length>200 ms favor the diagnosis of atrial flutter. In this case the electrogram facilitated the correct rhythm diagnosis for AI learning.

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  Figure 3. Annotation schematic. Surface ECG data acquisition was based on single‐lead ECG (→) and concurrent atrial electrogram signal acquisition derived from a pair of Teflon‐coated, stainless cardiac pacing lead (→), a, b were labeled as AF and c, d were labeled as non‐AF with the assistance of atrial signal; and then the atrial signal was eliminated and the annotated surface ECG data (a’, b’, c’, d’) platform was established and ready for AI training, validation, and testing. The designation “ADD & Norm” are known steps adopted by the transformer architecture that contribute to the stable training of the model as noted in Vaswani et al 20 and in He et al. 21 AAO indicates ascending aorta; CPL, cardiac pacing lead; RA, right atrium; and RV, right ventricle.

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  Figure 4. Atrial fibrillation (AF) burden for each patient record among learning and testing data sets. Patient data are displayed as sorted incrementally according to AF burden. A, Histograms of the learning and testing sets (x axis: the frequency of total AF burden; y axis: total AF burden for each patient). B and C, QQ plot of AF burden in the learning and testing data sets (x axis: observed value of total AF burden for each patient; y axis: expected normal value of total AF burden conforming to the normal distribution). The distribution of AF burden was the same across categories of patient sets, statistically tested by independent‐samples Kolmogorov‐Smirnov test (P=0.751).

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  Figure 5. Diagnostic performance of atrial fibrillation (AF) and AF burden in the validation set and testing set. A, The area under the receiver operating characteristic (ROC) curve in the validation set for AF recognition was 0.932. B, The area under the ROC curve in the testing set for AF recognition was 0.953. C, The intraclass correlation coefficient (ICC) for AF burden in the validation set was 0.983. D, The ICC of AF burden in the testing set was 0.952 based on artificial intelligence (AI) and the gold standard. AUC indicates area under the curve.