Evidence map›Paper›PMID 35040610›Full record

SynthesisYonsei medical journal2022

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.

Solam Lee, Yuseong Chu, Jiseung Ryu, Young Jun Park, Sejung Yang, Sang Baek Koh

2 registry-linked trialsOpen access · goldAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 50 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 10 pooled it
11.5field-weighted citation impact, top 1% of its field
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.

NCT05708846 completednot on this mapstarted 2023, after this paper: background citation

Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure

TypeobservationalSponsorhumanITcareRan2023 to 2024Enrolled154ConditionsHeart FailureArmsTelemonitoring
NCT05825755 nacompletednot on this mapstarted 2024, after this paper: background citation

Randomized Controlled Multicenter Trial for the Validation of Vitalera Platform

TypeinterventionalSponsorhumanITcareRan2024 to 2024Enrolled97ConditionsChronic Patients, Heart FailureArmsvitalera telemonitoring platform
3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 10 syntheses or guidelines pooled it, 73 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Solam Lee *Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0001-6458-9449
Yuseong Chu *Department of Biomedical Engineering, Yonsei University, Wonju, Korea.ORCID https://orcid.org/0000-0003-0930-4628
Jiseung RyuDepartment of Biomedical Engineering, Yonsei University, Wonju, Korea.ORCID https://orcid.org/0000-0001-6421-0160
Young Jun ParkDivision of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea.ORCID https://orcid.org/0000-0002-1461-5597
Sejung YangDepartment of Biomedical Engineering, Yonsei University, Wonju, Korea. syang@yonsei.ac.kr.ORCID https://orcid.org/0000-0002-5841-851X
Sang Baek KohDepartment of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea. kohhj@yonsei.ac.kr.ORCID https://orcid.org/0000-0001-5609-6521
Yonsei University · KR

Funding

Korea Health Industry Development Institute
6 · The paper itself

Abstract

purposeSeveral artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. MATERIALS AND

methodsThe searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.

resultsA total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983).

conclusionThis review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

Indexed as

Atrial FibrillationWearable Electronic DevicesAlgorithmsArtificial IntelligenceHumansSensitivity and Specificityartificial intelligencecardiovascular diseasedeep learningElectrocardiographymachine learningphotoplethysmography

Identifiers

PMID35040610
PMCPMC8790582
OpenAlexW4205432209

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.