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New AI framework digitizes paper ECGs for remote heart attack screening

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ECGLight, a compute-light framework designed to digitize paper electrocardiogram (ECG) printouts and screen for myocardial infarction (MI). This on-device system converts smartphone photos of ECGs into calibrated 12-lead signals, enabling diagnosis even in remote clinics with limited connectivity or computational resources. The framework achieves high accuracy, with 95.51% for MI detection on the PTB-XL dataset and 88.89% for OMI detection on the ECG-Matrix dataset, running in under 30 seconds per ECG on CPU-only resources. AI

IMPACT Enables AI-powered cardiac diagnostics in low-resource settings, democratizing access to critical health information.

RANK_REASON The cluster contains a research paper detailing a new AI framework for medical diagnosis.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI framework digitizes paper ECGs for remote heart attack screening

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shreyasvi Natraj, Cyrus Achtari, Felice Gragnano, Andrea Milzi, Marco Valgimigli, Diego Paez-Granados ·

    ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening

    arXiv:2607.07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited connectivity and computational capa…

  2. arXiv cs.LG TIER_1 English(EN) · Diego Paez-Granados ·

    ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening

    Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited connectivity and computational capacity. As a result, vast numbers of physical ECGs…

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