Enhancing Chest Radiography Artificial Intelligence Model with Electrocardiogram for Improved Detection of Low Ejection Fraction

Artificial intelligence models reportedly detect a low ejection fraction (EF) with chest radiography (CXR) examinations, which traditionally require a transthoracic echocardiogram (TTE) for evaluation. However, whether coupling a CXR model with an electrocardiogram (ECG) model improves the detection performance remains unclear. This study aimed to evaluate various models and fusion strategies for their detection performance.This study included 7,246 patients who underwent CXR, ECG, and TTE at the University of Tokyo Hospital. Two ECG models were used-a convolutional neural network and masked autoencoder. Two methods for combining CXR and ECG models were tested, early and late fusion. In the early fusion method, the CXR and ECG models were trained simultaneously, whereas in the late fusion method, three ensemble techniques were implemented. The CXR single model achieved an area under the curve (AUC) of 0.798. Both fusion models significantly outperformed the CXR single model. The early fusion model achieved an AUC of 0.937 (P = 0.015), while the late fusion model had an AUC of 0.928 (P = 0.010).Combining the CXR and ECG models significantly improved detection performance. This approach enables more accurate identification of patients with low EF, a condition typically requiring TTE for diagnosis.

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Publication Details

Journal
International Heart Journal
Published
2025-09-29
DOI
https://doi.org/10.1536/ihj.25-199
Primary Topic
COVID-19 diagnosis using AI
Type
article
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article

Enhancing Chest Radiography Artificial Intelligence Model with Electrocardiogram for Improved Detection of Low Ejection Fraction

Norihiko Takeda, Mike Saji, Katsuhito Fujiu, Hiroyuki Watanabe et al.
International Heart Journal
COVID-19 diagnosis using AI
article

Enhancing Chest Radiography Artificial Intelligence Model with Electrocardiogram for Improved Detection of Low Ejection Fraction

Norihiko Takeda, Mike Saji, Katsuhito Fujiu, Hiroyuki Watanabe, Hideo Fujita, Norifumi Takeda, Hiroshi Akazawa, Masao Takahashi, Nahoko Kato, Mamoru Nanasato, Shinnosuke Sawano, Hiroki Shinohara, Naoto Setoguchi, Shunichi Kushida, Satoshi Kodera, Hiroyuki Morita, Koki Nakanishi, Risa Kishikawa, Hisataka Maki, Jiro Ando, Kengo Tanabe, S Hamano, Masataka Satô, Naoko Sawada
article en

Abstract

Artificial intelligence models reportedly detect a low ejection fraction (EF) with chest radiography (CXR) examinations, which traditionally require a transthoracic echocardiogram (TTE) for evaluation. However, whether coupling a CXR model with an electrocardiogram (ECG) model improves the detection performance remains unclear. This study aimed to evaluate various models and fusion strategies for their detection performance.This study included 7,246 patients who underwent CXR, ECG, and TTE at the University of Tokyo Hospital. Two ECG models were used-a convolutional neural network and masked autoencoder. Two methods for combining CXR and ECG models were tested, early and late fusion. In the early fusion method, the CXR and ECG models were trained simultaneously, whereas in the late fusion method, three ensemble techniques were implemented. The CXR single model achieved an area under the curve (AUC) of 0.798. Both fusion models significantly outperformed the CXR single model. The early fusion model achieved an AUC of 0.937 (P = 0.015), while the late fusion model had an AUC of 0.928 (P = 0.010).Combining the CXR and ECG models significantly improved detection performance. This approach enables more accurate identification of patients with low EF, a condition typically requiring TTE for diagnosis.

International Heart JournalVol. 66(5)
Jichi Medical University (JP), NTT Medical Center (JP), University of Tokyo Hospital (JP), Sakakibara Hospital (JP), Mitsui Memorial Hospital (JP), Asahi General Hospital (JP), JR Tokyo General Hospital (JP), Jichi Medical University Saitama Medical Center (JP), The University of Tokyo (JP)
Openalex Percentile: Top 60%
COVID-19 diagnosis using AI
DOIOpen Access PDFOpenAlex
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