Supplementary Material for: Detecting atrial fibrillation by artificial intelligence enabled neuroimaging examination.

Background

Diagnosis of occult atrial fibrillation (AF) is difficult as it is often asymptomatic, leading to under detection. Current diagnostic tests have variable limitations in feasibility and accuracy. Machine learning is gaining greater traction for clinical decision making and may help facilitate the detection of undiagnosed AF when applied to magnetic resonance imaging (MRI). We hypothesise that machine learning algorithm increases the accurate classification of MRIs of stroke patients into those due to AF vs large artery atherosclerosis.

Methods

Stroke aetiology for each patient was determined by a review of medical records and investigations. Patients with either AF or large artery atherosclerosis were included.
Patients were randomly divided into the training and validation groups (4:1). A 3D convolutional neural network (ConvNeXt) was developed to train and validate the algorithm. After training, the models were evaluated using common metrics for binary classification.

Results

A total of 235 patients were analysed (97 with AF, 138 without AF). The mean age of the sample was 71.1 (SD 14.2) and 35% percent were female. The best discriminative performance was obtained in the 5th fold of cross-validation (AUC-ROC 0.88) and the overall model performance was 0.81. The best performing metrics were precision (0.84) and the F1-score (0.77).

Conclusion

Our machine learning algorithm has reasonable classification power in categorizing stroke patients into those with and without underlying AF. Testing in external validation data sets are critical to confirm these results.