AI Identifies Clinically Relevant Asystole Among False Loop Recorder Alerts: From Data Overload to Diagnosis

Implantable loop recorders (ILRs) are often implanted to investigate unexplained syncopal episodes. However, ILRs may produce false alerts due to undersensing or artifacts, thus requiring careful review of numerous device recordings, which makes it challenging to identify clinically significant events.

We report a case of a 77-year-old woman with recurrent syncopal episodes, in whom a 7-second pause was detected by an ILR. Device interrogations showed multiple pause alerts that were identified as false events because of poor R sensing. However, the artificial intelligence (AI)-enhanced remote monitoring identified a true pause with blocked P waves, leading the patient to receive a pacemaker implantation.

This case highlights the clinical value of AI-assisted remote monitoring in identifying clinically relevant arrhythmias among numerous device alerts. Improved automated filtering of ILR data may reduce diagnostic delays and help clinicians prioritize clinically significant events.

ILRs may generate numerous false alerts because of sensing abnormalities, complicating the identification of clinically relevant events in routine practice. AI-enhanced remote monitoring may improve diagnostic efficiency by identifying true arrhythmic events within large volumes of device-generated data and guiding timely management.