Evidence map›Paper›PMID 41329232›Full record

ArticleInternal and emergency medicine2026

Non-invasive hemoglobin estimation with outcome prediction via deep learning analysis of ECG-derived cardiac micro-dynamics.

Chung-Chi Yang, Chin-Sheng Lin, Chin Lin, Wei-Ting Liu, Zih-Yin Lai, Yung-Jen Chuang

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Article in Internal and emergency medicine, 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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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chung-Chi YangDivision of Cardiovascular Medicine, Taoyuan Armed Forces General Hospital, Taoyuan, 32551, Taiwan, ROC.
Chin-Sheng LinDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Chin LinMedical Technology Education Center, College of Medicine, National Defense Medical University, Taipei, Taiwan, ROC.
Wei-Ting LiuDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Zih-Yin LaiSchool of Medicine, National Tsing Hua University, Hsinchu, 300044, Taiwan, ROC.
Yung-Jen ChuangSchool of Medicine, National Tsing Hua University, Hsinchu, 300044, Taiwan, ROC. yjchuang@life.nthu.edu.tw.ORCID 0000-0002-6022-0107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnemia is a global health issue, especially in resource-limited areas, where traditional hemoglobin (Hb) testing is invasive and costly. This study aimed to develop an electrocardiogram-hemoglobin (ECG-Hb) deep learning model (DLM) for detecting anemia and assess its impact on all-cause mortality and new-onset heart failure.

methodsThis retrospective study analyzed ECGs and corresponding Hb levels from two hospitals. The DLM was trained on 388,166 ECGs from 187,202 patients and tested on 24,279 and 29,247 patients in internal and external sets, respectively. Anemia was defined as moderate (Hb ≤ 10 g/dL) or severe (Hb ≤ 8 g/dL). Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, and an 8-year follow-up assessed mortality and heart failure risk with Cox regression.

resultsThe areas under the ROC curves (AUCs) for detecting moderate-to-severe anemia were 0.8545 (internal) and 0.8243 (external), with sensitivities of 65.9% and 71.0%, and specificities of 84.8% and 77.4%, respectively. ECG-Hb performed better in detecting severe anemia (AUC = 0.9038/0.8766) than in mild anemia. Pearson correlations between ECG-Hb and Hb were 0.56 (internal) and 0.53 (external). Key ECG features, including heart rate variability, significantly influenced ECG-Hb. Patients with severely low ECG-Hb had higher risks of mortality (hazard ratio [HR]: 1.71, 95% confidence interval [CI]: 1.42-2.06) and heart failure (HR: 2.47, 95% CI: 2.07-2.94) compared to those with standard ECG-Hb levels.

conclusionThe ECG-Hb DLM offers strong diagnostic and prognostic potential for anemia and cardiovascular risks, making it a valuable, non-invasive screening tool in low-resource settings.

Indexed as

AnemiaDeep LearningElectrocardiographyHemoglobinsAgedFemaleHeart FailureHumansMaleMiddle AgedRetrospective StudiesROC CurveHemoglobinsAnemia detectionDeep learning modelElectrocardiogramHeart rate variabilityPrognostic value

Identifiers

PMID41329232
PMCPMC13061774

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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 OpenQuestion graph.