Explainable Artificial Intelligence Model for Diagnosis of Atrial Fibrillation Using Holter Electrocardiogram Waveforms.

Atrial fibrillation is a clinically important arrhythmia. There are some reports on machine learning models for AF diagnosis using electrocardiogram data. However, few reports have proposed an eXplainable Artificial Intelligence (XAI) model to enable physicians to easily understand the machine learning model's diagnosis results.We developed and validated an XAI-enabled atrial fibrillation diagnosis model based on a convolutional neural network (CNN) algorithm. We used Holter electrocardiogram monitoring data and the gradient-weighted class activation mapping (Grad-CAM) method.Electrocardiogram data recorded from patients between January 4, 2016, and October 31, 2019, totaling 57,273 electrocardiogram waveform slots of 30 seconds each with diagnostic information annotated by cardiologists, were used for training our proposed model. Performance metrics of our AI model for AF diagnosis are as follows: sensitivity, 97.1% (95% CI: 0.969-0.972); specificity, 94.5% (95% CI: 0.943-0.946); accuracy, 95.3% (95% CI: 0.952-0.955); positive predictive value, 89.3% (95% CI: 0.892-0.897); and F-value, 93.1% (95% CI: 0.929-0.933). The area under the receiver operating characteristic curve for AF detection using our model was 0.988 (95% CI: 0.987-0.988). Furthermore, using the XAI method, 94.5 ± 3.5% of the areas identified as regions of interest using our machine learning model were identified as characteristic sites for AF diagnosis by cardiologists.AF was accurately diagnosed and favorably explained with Holter ECG waveforms using our proposed CNN-based XAI model. Our study presents another step toward realizing a viable XAI-based detection model for AF diagnoses for use by physicians.

How COVID-19 Affected the Introduction of Telemedicine and Patient Reported Outcomes Among Patients With Pulmonary Hypertension　- A Report From a Referral Center in Japan.

Background: COVID-19 is fatal to patients with pulmonary hypertension (PH), so preventive actions are recommended. This study investigated the effectiveness of telemedicine and effects on quality of life (QOL) in the treatment of patients with PH. Methods and Results: Japanese patients with PH (n=40) were recruited from one referral center. Patient self-reported anxiety worsened significantly and elderly patients in particular experienced detrimental lifestyle changes under COVID-19. Telemedicine worked well to decrease the frequency of going out. Conclusions: Telemedicine is effective in reducing travel distances, and frequent remote interventions may be desirable for older, anxious patients.