Health information science and systems · 2026
Language-supervised electrocardiogram interpretation with vision transformer.
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Takei Makoto, Harada Kazumasa, Shiraishi Yasuyuki, Matsuda Junya, Iwasaki Yoichi, Yamamoto Yoshiya, Matsushita Kenichi, Miyazaki Tetsuro, Miyamoto Takamichi, Iida Kiyosi, Tanimoto Shuzo, Nagatomo Yuji, Hosoda Toru, Kohsaka Shun, Yamamoto Takeshi, Nagao Ken, Takayama Morimasa
Аннотация
PURPOSE: Most artificial intelligence models for electrocardiogram (ECG) interpretation rely on task-specific supervised learning, requiring dedicated labeled datasets for each diagnostic task and limiting scalability across diverse cardiac conditions. This study investigated whether vision-language learning from paired ECG recordings and summary reports could enable transferable ECG representations without task-specific supervised training.
METHODS: We developed ECG-VLM, a contrastive vision-language model consisting of a Transformer-based image encoder and a text encoder, trained on 800,034 paired 10-s ECG recordings and summary reports from the MIMIC-IV-ECG database. Cardiac condition detection was performed using natural-language prompts without condition-specific retraining or fine-tuning. Performance was evaluated on an internal test set and externally validated using the LSAS-ECG dataset (45,151 recordings) across 14 cardiac conditions, together with retrieval and subgroup analysis.
RESULTS: ECG-VLM achieved diagnostic performance across 14 cardiac conditions, with AUROC ranging from 0.679 to 0.981 on the internal test set and from 0.691 to 0.991 on external validation. Performance was strongest for rhythm abnormalities (mean AUROC 0.948 internally and 0.921 externally) and lower for morphological conditions. Compared with the previously reported signal-based ECG-CLIP framework, ECG-VLM achieved comparable performance for rhythm abnormalities while improving AUPRC for several morphological conditions. Performance remained broadly consistent across external validation and demographic and clinical subgroups.
CONCLUSION: These findings demonstrate the feasibility of vision-language supervision from paired ECG recordings and summary reports for learning transferable ECG representations without task-specific supervised training. This framework provides a basis for future extension of vision-language learning to ECG images encountered in routine clinical practice.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00482-7.
METHODS: We developed ECG-VLM, a contrastive vision-language model consisting of a Transformer-based image encoder and a text encoder, trained on 800,034 paired 10-s ECG recordings and summary reports from the MIMIC-IV-ECG database. Cardiac condition detection was performed using natural-language prompts without condition-specific retraining or fine-tuning. Performance was evaluated on an internal test set and externally validated using the LSAS-ECG dataset (45,151 recordings) across 14 cardiac conditions, together with retrieval and subgroup analysis.
RESULTS: ECG-VLM achieved diagnostic performance across 14 cardiac conditions, with AUROC ranging from 0.679 to 0.981 on the internal test set and from 0.691 to 0.991 on external validation. Performance was strongest for rhythm abnormalities (mean AUROC 0.948 internally and 0.921 externally) and lower for morphological conditions. Compared with the previously reported signal-based ECG-CLIP framework, ECG-VLM achieved comparable performance for rhythm abnormalities while improving AUPRC for several morphological conditions. Performance remained broadly consistent across external validation and demographic and clinical subgroups.
CONCLUSION: These findings demonstrate the feasibility of vision-language supervision from paired ECG recordings and summary reports for learning transferable ECG representations without task-specific supervised training. This framework provides a basis for future extension of vision-language learning to ECG images encountered in routine clinical practice.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00482-7.