Evidence map›Paper›PMID 41776062›Full record

ArticleNPJ cardiovascular health2025

Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals.

Youngtae Kim, Tae Gwan Jang, So Yeon Park, Ha Young Park, Ji Ae Lee, Tumenbat Oyun-Erdene, Sang-Ha Kim, Young Jun Park, Sung Pil Cho, Junghwan Park and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Youngtae KimArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
Tae Gwan JangArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
So Yeon ParkArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
Ha Young ParkArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
Ji Ae LeeArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
Tumenbat Oyun-ErdeneArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea.
Sang-Ha KimDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Wonju Severance Christian Hospital, Wonju, 26426, Republic of Korea.
Young Jun ParkDivision of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Wonju, 26426, Republic of Korea.
Sung Pil ChoMEZOO Co., Ltd, Wonju, Republic of Korea.
Junghwan ParkMEZOO Co., Ltd, Wonju, Republic of Korea.
Dongwon KangMEDIANA Co., Ltd, Wonju, Republic of Korea.
Erdenebayar UrtnasanArtificial Intellenge Big Data Medical Center, Wonju College of Medicine, Yonsei University Mirae Campus, Wonju, 26426, Republic of Korea. edenbyra@yonsei.ac.kr.

Funding

This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education 2022RIS-005This work was partially supported by the Korea Medical Device Development Fund grant funded by the Korea government RS-2020-KD000030
6 · The paper itself

Abstract

This study proposes a multimodal AI algorithm called the SleepCVD-Net to automatically screen CVDs based on nocturnal physiological recordings. We designed and implemented a multimodal AI algorithm, SleepCVD-Net, which utilizes three-mode deep neural networks to process input signals-single-lead electrocardiography (ECG), Airflow, and oxygen saturation (SpO

Identifiers

PMID41776062
PMCPMC12912297

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.