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AI models show promise for detecting atrial fibrillation in ICU patients

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new dataset and benchmarks for detecting atrial fibrillation (AF) in intensive care unit (ICU) patients using electrocardiograms (ECGs). The study compared three AI approaches: feature-based classifiers, deep learning, and ECG foundation models (FMs). ECG FMs demonstrated superior performance, achieving the highest F1 score of 0.89 with a transfer learning strategy on the ICU test set. This work aims to advance automated patient monitoring systems by providing a valuable dataset and performance benchmarks for the research community. AI

IMPACT This research could lead to improved automated patient monitoring systems for cardiac arrhythmias in critical care settings.

RANK_REASON The cluster is based on an academic paper detailing a new dataset and benchmarks for AI-driven medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models show promise for detecting atrial fibrillation in ICU patients

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27 / 100
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The cluster is based on an academic paper detailing a new dataset and benchmarks for AI-driven medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina, Shamel Addas, Stephanie Sibley, Gabor Fichtinger, David Pichora, David Maslove, Purang Abolmaesumi, Parvin Mousavi ·

    A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients

    arXiv:2512.18031v2 Announce Type: replace Abstract: Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we publish a labelled ICU dataset and benchmarks fo…

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