ArticleArchivos de cardiologia de Mexico2025
Derivation of an artificial intelligence-based electrocardiographic model for the detection of acute coronary occlusive myocardial infarction.
Article in Archivos de cardiologia de Mexico, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence-Based Electrocardiography for the Detection of Heart Diseases: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Long-Term Outcomes in NSTEMI Patients Based on Coronary TIMI Flow State on Presentation.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: We aimed to assess the performance of an artificial intelligence-electrocardiogram (AI-ECG)-based model capable of detecting acute coronary occlusion myocardial infarction (ACOMI) in the setting of patients with acute coronary syndrome (ACS). Methods: This was a prospective, observational, longitudinal, and single-center study including patients with the initial diagnosis of ACS (both ST-elevation acute myocardial infarction [STEMI] & non-ST-segment elevation myocardial infarction [NSTEMI]). To train the deep learning model in recognizing ACOMI, manual digitization of a patient's ECG was conducted using smartphone cameras of varying quality. We relied on the use of convolutional neural networks as the AI models for the classification of ECG examples. ECGs were also independently evaluated by two expert cardiologists blinded to clinical outcomes; each was asked to determine (a) whether the patient had a STEMI, based on universal criteria or (b) if STEMI criteria were not met, to identify any other ECG finding suggestive of ACOMI. ACOMI was defined by coronary angiography findings meeting any of the following three criteria: (a) total thrombotic occlusion, (b) TIMI thrombus grade 2 or higher + TIMI grade flow 1 or less, or (c) the presence of a subocclusive lesion (> 95% angiographic stenosis) with TIMI grade flow < 3. Patients were classified into four groups: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + non-ACOMI, and NSTEMI + non-ACOMI. Results: For the primary objective of the study, AI outperformed human experts in both NSTEMI and STEMI, with an area under the curve (AUC) of 0.86 (95% confidence interval [CI] 0.75-0.98) for identifying ACOMI, compared with ECG experts using their experience (AUC: 0.33, 95% CI 0.17-0.49) or under universal STEMI criteria (AUC: 0.50, 95% CI 0.35-0.54), (p value for AUC receiver operating characteristic comparison < 0.001). The AI model demonstrated a PPV of 0.84 and an NPV of 1.0. Conclusion: Our AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and the use of STEMI criteria. Further research and external validation are needed to understand the role of AI-based models in the setting of ACS.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.