AI-ECG AF Model
To tackle the complex problem of screening for AF, especially pertaining to paroxysmal and SCAF, our group developed a unique model to predict the likelihood of a person having underlying hidden AF from a sinus rhythm ‘apparently normal’ ECG without any additional information.11 The rationale for this study was that mechanical remodeling in the form of myocyte hypertrophy, fibrosis and chamber enlargement might lead to ECG changes yet undefined and too subtle to be studied effectively by human potential. For instance, evidence of interatrial block (Bayés syndrome) which is seldom reported on ECGs correlates to both risk of incident AF and stroke.50,51
Over 20 years of ECG data from 180,922 patients and 649,931 normal sinus rhythm ECGs were analyzed. Patients were randomly assigned into 3 groups- 70% for training dataset, 10% for internal validation (optimization and selecting hyperparameters) and 20% testing set (previously unexposed ECGs). Within each dataset there were 2 groups- patients with at least one ECG confirmed AF/atrial flutter diagnosis and patients with no AF rhythms recorded. A 31-day window period preceding the AF recorded ECG was taken in the disease group and all sinus rhythm ECGs were included from the control group. This short 31-day period prior to AF diagnosis was taken to include ECGs with the maximum potential markers associated with AF and left atrial remodeling.
To optimize performance, eight independent leads (leads I, II, and V1–6) were selected because any linear function of the leads could be learned by the models (8×5000 matrix). The model was then tested on a dataset of 130,802 sinus rhythm ECGs (3051 verified AF cases) (Table 1). Model performed well with an AUC of 0·87 when a single ECG was used with no additional information and an AUC of 0.90 when multiple ECGs were used.