ArticleCureus2026
Artificial Intelligence-Assisted ECG in a Hub-and-Spoke Network in India: Real-World Performance in Acute Coronary Syndrome Detection and Diagnostic Turnaround Times.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence in cardiology: implications for healthcare outcomes.Frontiers in artificial intelligence · 2026Review
- Artificial Intelligence-Empowered "Walking Hospitals": A Narrative Review of Innovative Models and Ecosystem Construction in Rural Healthcare.International journal of general medicine · 2026Review
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Authors and funding
8 authors.
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Abstract
introductionAcute coronary syndrome (ACS) remains a leading cause of morbidity and mortality in India, with delayed diagnosis contributing to poor outcomes. The 'Heart Beat' project - an AI-assisted hub-and-spoke model was implemented to improve early and accurate ACS detection across diverse healthcare settings. In this retrospective, real-world observational analysis, we leveraged data from this initiative to (1) assess the prevalence of cardiovascular diseases (CVD) in the Indian population using AI-powered electrocardiogram (ECG) interpretation and (2) evaluate the turnaround time (TAT) for ECG performance and diagnosis.
methodsThis report utilized data from the Heart Beat project, collected between January 2020 and December 2022. We included patients presenting with chest pain at primary healthcare centers (spokes), linked to centrally located hospitals (
resultsA total of 45,488 ECGs were obtained from 66 spokes, which were connected to 10 hubs across six Indian states. The most commonly reported ECGs were Normal (n=22987, 50.53%), Abnormal (n=19396, 42.64%), and Critical (n=2992, 6.58%). Most of the CVD conditions were diagnosed from Critical and Abnormal ECGs. Out of the seven CVD conditions analyzed from the dataset, left ventricular hypertrophy (LVH; n=3993, 8.78%) was the most frequently reported. Higher ST-elevation myocardial infarction (STEMI) was identified in 231 cases (0.51% of total ECGs), whereas non-STEMI (NSTEMI) was identified in 22 cases (0.05% of total ECGs) among the total ECGs recorded. The mean (SD) TAT for ECGs of all patients was 5.12 (9.61) minutes, with the minimum mean TAT recorded for Critical (2.91 minutes) and the maximum for Normal (5.61 minutes) ECGs.
conclusionThe findings highlight a higher prevalence of STEMI over NSTEMI cases, emphasizing the need for adequate resources at healthcare centers for timely and effective management. An average lower ECG TAT (5 minutes) further supported AI-assisted ECG's potential in the early diagnosis of ACS patients.
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