A deep learning algorithm for detecting acute myocardial infarction

Wencheng Liu, Chin‐Sheng Lin, Chien‐Sung Tsai, Tien‐Ping Tsao, Cheng‐Chung Cheng, Jun‐Ting Liou, Wei‐Shiang Lin, Shu‐Meng Cheng et al.

EuroIntervention 2021 article cited by 93

Source Open-access PDF More in Medicine & Health

importance 8/10 ai assisted ECG Monitoring and Analysis cardiology stage: research

Problem

Acute myocardial infarction is delayed or misdiagnosed in daily practice, and 12-lead ECG interpretation needs strengthening.

Why it matters

Delayed or missed AMI diagnosis is common and clinically dangerous.

What the AI did

A deep learning model was trained on over 142,000 ECGs (STEMI/NSTEMI and non-AMI patients) to detect AMI and was evaluated in a human-machine competition.

Results

AUC 0.976 for STEMI detection in the human-machine competition

Impact

Retrospective validation suggests the DLM could support ECG-based AMI diagnosis in emergency departments.

Record

Venue	EuroIntervention
Published	2021
Work type	article
Authors	Wencheng Liu, Chin‐Sheng Lin, Chien‐Sung Tsai, Tien‐Ping Tsao, Cheng‐Chung Cheng, Jun‐Ting Liou, Wei‐Shiang Lin, Shu‐Meng Cheng et al.
Citations	93 (OpenAlex, at import time)
References	33
Model or method	deep learning model (DLM)
Organization	not stated in source
Primary source	https://doi.org/10.4244/eij-d-20-01155
Open access	green
Keywords	deep learning, ECG, myocardial infarction, STEMI, diagnosis

Importance rating

8 / 10

major — millions of lives, an industry or a field

AMI is a common, clinically dangerous emergency where delayed/misdiagnosis occurs; DLM beat physicians (AUC 0.976) on 142k+ ECGs, supporting emergency diagnosis, though abstract states no mortality scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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