Multimodal deep learning model for enhanced early detection of aortic stenosis integrating ECG and chest x-ray with cooperative learning

The multimodal AI model via cooperative learning outperformed single-modality methods in AS detection and may aid earlier diagnosis and reduce clinical burden.

Authors

Institutions

Publication Details

Journal
Frontiers in Radiology
Published
2025-11-25
DOI
https://doi.org/10.3389/fradi.2025.1698680
Citations
1
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
1.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multimodal deep learning model for enhanced early detection of aortic stenosis integrating ECG and chest x-ray with cooperative learning

Makoto Nishimori, Shun Nagai, Hiromasa Otake, Masakazu Shinohara et al.
1 citations
Frontiers in Radiology
ECG Monitoring and Analysis
1.39
article

Multimodal deep learning model for enhanced early detection of aortic stenosis integrating ECG and chest x-ray with cooperative learning

Makoto Nishimori, Shun Nagai, Hiromasa Otake, Masakazu Shinohara, Hidekazu Tanaka
article en
1 citations

Abstract

The multimodal AI model via cooperative learning outperformed single-modality methods in AS detection and may aid earlier diagnosis and reduce clinical burden.

Frontiers in RadiologyVol. 5
Kobe University (JP)
Openalex Percentile: Top 13%
ECG Monitoring and Analysis
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.