A NOVEL AI ALGORITHM APPLIED TO ILR TRANSMISSIONS: PERFORMANCE AND AGREEMENT RATES WITH HEALTHCARE PROFESSIONALS

BackgroundThe use of implantable Loop Recorders (ILRs) is increasing rapidly but generates a high workload for timely adjudication of transmissions. This burden is aggravated by excessive false positive alerts. We developed a novel artificial intelligence (AI)-based algorithm to reclassify ILR episodes aiming to decrease False Positive rate by 80% while maintaining 99% sensitivity.ObjectiveTo evaluate the performance of the algorithm in real-life conditions.MethodsMedtronic Reveal LINQ ILR patients were enrolled from 40 European medical centers. Using a cloud based remote monitoring platform, all episodes detected as “Abnormal” by ILRs were re-diagnosed by the AI algorithm. Healthcare professionals (HCPs) monitoring their patients on this platform could review the diagnosis suggested by the algorithm and then attribute a new diagnosis to any episode. The positive predictive value (PPV) and negative predictive value (NPV) of the algorithm detecting “Abnormal” episodes were evaluated with healthcare professionals’ diagnoses, excluding uncertain diagnoses (artifact, lead noise, atrial and ventricular monitoring).ResultsAmong 10,296 “Abnormal” LINQ episodes analyzed by the AI algorithm and checked by HCPs, 4,442 (43.1%) were diagnosed as “Normal Rhythm” by the algorithm. HCPs submitted a certain diagnosis on 772 of these episodes, among which 750 were “Normal Rhythm”, resulting in a NPV of 97.1%. The other 5,854 (56.9%) “Abnormal” diagnoses were confirmed by the AI algorithm. HCPs submitted a certain diagnosis on 834 of these episodes, among which 773 were “Abnormal”, resulting in a PPV of 92.7%.Conclusion BackgroundThe use of implantable Loop Recorders (ILRs) is increasing rapidly but generates a high workload for timely adjudication of transmissions. This burden is aggravated by excessive false positive alerts. We developed a novel artificial intelligence (AI)-based algorithm to reclassify ILR episodes aiming to decrease False Positive rate by 80% while maintaining 99% sensitivity. The use of implantable Loop Recorders (ILRs) is increasing rapidly but generates a high workload for timely adjudication of transmissions. This burden is aggravated by excessive false positive alerts. We developed a novel artificial intelligence (AI)-based algorithm to reclassify ILR episodes aiming to decrease False Positive rate by 80% while maintaining 99% sensitivity. ObjectiveTo evaluate the performance of the algorithm in real-life conditions. To evaluate the performance of the algorithm in real-life conditions. MethodsMedtronic Reveal LINQ ILR patients were enrolled from 40 European medical centers. Using a cloud based remote monitoring platform, all episodes detected as “Abnormal” by ILRs were re-diagnosed by the AI algorithm. Healthcare professionals (HCPs) monitoring their patients on this platform could review the diagnosis suggested by the algorithm and then attribute a new diagnosis to any episode. The positive predictive value (PPV) and negative predictive value (NPV) of the algorithm detecting “Abnormal” episodes were evaluated with healthcare professionals’ diagnoses, excluding uncertain diagnoses (artifact, lead noise, atrial and ventricular monitoring). Medtronic Reveal LINQ ILR patients were enrolled from 40 European medical centers. Using a cloud based remote monitoring platform, all episodes detected as “Abnormal” by ILRs were re-diagnosed by the AI algorithm. Healthcare professionals (HCPs) monitoring their patients on this platform could review the diagnosis suggested by the algorithm and then attribute a new diagnosis to any episode. The positive predictive value (PPV) and negative predictive value (NPV) of the algorithm detecting “Abnormal” episodes were evaluated with healthcare professionals’ diagnoses, excluding uncertain diagnoses (artifact, lead noise, atrial and ventricular monitoring). ResultsAmong 10,296 “Abnormal” LINQ episodes analyzed by the AI algorithm and checked by HCPs, 4,442 (43.1%) were diagnosed as “Normal Rhythm” by the algorithm. HCPs submitted a certain diagnosis on 772 of these episodes, among which 750 were “Normal Rhythm”, resulting in a NPV of 97.1%. The other 5,854 (56.9%) “Abnormal” diagnoses were confirmed by the AI algorithm. HCPs submitted a certain diagnosis on 834 of these episodes, among which 773 were “Abnormal”, resulting in a PPV of 92.7%. Among 10,296 “Abnormal” LINQ episodes analyzed by the AI algorithm and checked by HCPs, 4,442 (43.1%) were diagnosed as “Normal Rhythm” by the algorithm. HCPs submitted a certain diagnosis on 772 of these episodes, among which 750 were “Normal Rhythm”, resulting in a NPV of 97.1%. The other 5,854 (56.9%) “Abnormal” diagnoses were confirmed by the AI algorithm. HCPs submitted a certain diagnosis on 834 of these episodes, among which 773 were “Abnormal”, resulting in a PPV of 92.7%. Conclusion