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ILR ECG Analyzer Detects Meaningful Cardiac Events

By HospiMedica International staff writers
Posted on 27 Dec 2021
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Image: AI can help detect true AF events recorded on an ILR (Photo courtesy of Implicity/ AdobeStock)
Image: AI can help detect true AF events recorded on an ILR (Photo courtesy of Implicity/ AdobeStock)
A new artificial intelligence (AI)-powered algorithm reduces the volume of false positives from implantable loop recorders (ILRs) by nearly 80%.

The Implicity (Cambridge, MA, USA) ILR electrocardiogram (ECG) rhythm data analyzer is designed for the Medtronic (Dublin, Ireland) Reveal LINQ, Reveal XT, and Reveal DX models. The digital device, which is integrated into the Implicity cardiac remote monitoring platform, will add additional signal processing and analysis to automatically classify and help prioritize true events. Studies have shown the Implicity algorithm reduced the number of false-positive episodes by 79% while maintaining 99% sensitivity.

“I am excited by the potential this technology has to transform the practice of remote patient monitoring. The true promise of remote monitoring is that we can see patients who need to be seen earlier,” said Professor Niraj Varma, MD, PhD, of the Cleveland Clinic (OH, USA). “Even if the patient is not experiencing a symptom, we can detect their condition and let them know they need to be seen. Implicity's innovative solutions will enable us to direct our attention to clinically actionable data better so we can do just that.”

"Excessive false positives from ILRs are very common. There's a high positive rate with many events that are considered arrhythmias, that in fact, are not arrhythmias,” said electrophysiologist Arnaud Lazarus, MD, of Clinique Ambroise Paré (Toulouse, France). “These false positives significantly burden clinicians who need to sift through the noise and adjudicate misclassified abnormalities. Implicity's algorithm significantly reduces this workload.”

ILR devices generate an increasing volume of data, necessitating medical staff to spend significant time processing a high number of false-positive atrial fibrillation detections. Medtronic has recently announced an AI algorithm to address the false positive issue, but it is only for use with the LINQ II model.

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