Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study

Quartieri, Fabio; Marina-Breysse, Manuel; Pollastrelli, Annalisa; Paini, Isabella; Lizcano, Carlos; Lillo-Castellano, José María; Grammatico, Andrea

doi:10.1016/j.cvdhj.2022.07.071

Cited by 11 publications

(6 citation statements)

References 24 publications

(55 reference statements)

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“…These devices showed different diagnostic accuracy ranging from 90% to 100% according to the type of detected arrhythmias and the calculation methods (Table 4). 9,20–22 …”

Section: Resultsmentioning

confidence: 99%

False‐positive alarms in patients with implantable loop recorder followed by remote monitoring: A systematic review

Covino,

Russo

2024

Pacing Clinical Electrophis

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Remote Monitoring (RM) has been shown to provide useful information about arrhythmic events in patients with implantable loop recorders (ILRs), however there is few and conflicting data about the false positive (FP) alarms burden and characteristics among ILR recipients. The aim of the present systematic review was to evaluate incidence and characteristics of FP alarms among ILR patients followed by RM. We developed a systematic research in Embase, MEDLINE and PubMed databases and selected all papers focused on false positive ILR transmissions published from June 01, 2013 to June 01, 2023. Case reports, meeting summaries, posters and simple reviews were excluded. Twelve reports were finally selected, including five prospective and seven retrospective studies. Information about population characteristics, device type and setting, overall transmissions and FP alarms and any adopted strategies to reduce them were extracted from an overall population of 3.305 patients. FP alarms were 59.7% of the overall remote transmissions and were found in 1/5 of the analyzed population. FP alarms for atrial fibrillation were the most common cause of false transmissions and were mainly due to premature atrial and ventricular complexes. No clinical predictors of FP alarms were identified, except for nonparasternal ILR implantation site. Since the overload work due to FP alarms might reduce the benefit of remote monitoring of ILR patients, the device optimization is an important step until an help from machine‐learning algorithms is available.

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“…These devices showed different diagnostic accuracy ranging from 90% to 100% according to the type of detected arrhythmias and the calculation methods (Table 4). 9,20–22 …”

Section: Resultsmentioning

confidence: 99%

False‐positive alarms in patients with implantable loop recorder followed by remote monitoring: A systematic review

Covino,

Russo

2024

Pacing Clinical Electrophis

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“…Finally, we examined 2 ILRs that have reprogramming ability; additional ILRs with algorithms to reduce false positives have been developed by other manufacturers and may have additional clinical utility compared with earlier‐generation ILRs. 15 , 16 , 17 …”

Section: Discussionmentioning

confidence: 99%

Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False‐Positive Alerts

Neiman,

Raitt,

Rohrbach

et al. 2024

JAHA

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Background Implantable loop recorders (ILRs) are increasingly placed for arrhythmia detection. However, historically, ≈75% of ILR alerts are false positives, requiring significant time and effort for adjudication. The LINQII and LUX‐Dx are remotely reprogrammable ILRs with dual‐stage algorithms using artificial intelligence to reduce false positives, but their utility in routine clinical practice has not been studied. Methods and Results We identified patients with the LINQII and LUX‐Dx who were monitored by the Veterans Affairs National Cardiac Device Surveillance Program between March and June 2022. ILR programming was customized on the basis of implant indication. All alerts and every 90‐day scheduled transmissions were manually reviewed. ILRs were remotely reprogrammed, as appropriate, after false‐positive alerts or 2 consecutive same‐type alerts, unless there was ongoing clinical need for that alert. Outcomes were total number of transmissions and false positives. We performed medical record review to determine if patients experienced any adverse clinical events, including hospitalization and mortality. Among 117 LINQII patients, there were 239 total alerts, 43 (18.0%) of which were false positives. Among 105 LUX‐Dx patients, there were 300 total alerts, 115 (38.3%) of which were false positives. LINQIIs were reprogrammed 22 times, resulting in a decrease in median alerts/day from 0.13 to 0.03. LUX‐Dx ILRs were reprogrammed 52 times, resulting in a decrease from 0.15 to 0.01 median alerts/day. There were no adverse clinical events that could have been identified by superior or earlier arrhythmia detection. Conclusions ILRs with artificial intelligence algorithms and remote reprogramming ability are associated with reduced alert burden because of higher true‐positive rates than prior ILRs, without missing potentially consequential arrhythmias.

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“…Sensitivity measures the model's ability to accurately identify individuals with mental health conditions. A perfect model should have high sensitivity and specificity to reduce false positives and negatives (Quartieri et al., 2022).…”

Section: Accuracy and Validity Of Ai In Mental Health Diagnosismentioning

confidence: 99%

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

Wimbarti,

Kairupan,

Tallei

2024

Int J Mental Health Nurs

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The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI‐driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well‐being within the confines of their homes. Nonetheless, the widespread trend of self‐diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self‐diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI‐driven self‐diagnosis.

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Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study

Cited by 11 publications

References 24 publications

False‐positive alarms in patients with implantable loop recorder followed by remote monitoring: A systematic review

False‐positive alarms in patients with implantable loop recorder followed by remote monitoring: A systematic review

Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False‐Positive Alerts

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

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