Artificial intelligence-based detection of atrial fibrillation from chest radiographs

Objective The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs. Methods This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF-positive or AF-negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning-based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset. Results The training dataset included 11,105 images (5637 patients; 3145 male, mean age +/- standard deviation, 68 +/- 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 +/- 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 +/- 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78-0.85) and 0.80 (0.76-0.84), sensitivity of 0.76 (0.70-0.81) and 0.70 (0.64-0.76), specificity of 0.75 (0.72-0.77) and 0.74 (0.72-0.77), and accuracy of 0.75 (0.72-0.77) and 0.74 (0.71-0.76). Conclusion Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF.

Automated summary

This study aimed to develop an artificial intelligence model to detect features of atrial fibrillation on chest radiographs. By training, tuning, and evaluating the model on different datasets, the study demonstrated the effectiveness and accuracy of the AI in identifying AF.