Evidence map›Paper›PMID 36388959›Full record

ArticleiScience2022

Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance.

Yunqing Liu, Chengjin Qin, Chengliang Liu, Jinlei Liu, Yanrui Jin, Zhiyuan Li, Liqun Zhao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 20% 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.

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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Yunqing LiuSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Chengjin QinSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Chengliang LiuSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Jinlei LiuSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Yanrui JinSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Zhiyuan LiSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
Liqun ZhaoDepartment of Cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, 100, Haining Road, Shanghai 200080, China.
Shanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, due to lack of large-scale datasets containing multiple arrhythmias and acute coronary syndrome-related diseases, AI-aided diagnosis for cardiac diseases is limited in clinical scenarios. Whether AI-based ECG diagnosis can assist cardiologists to improve performance has not been reported. We constructed a large-scale dataset containing multiple high-regional-incidence arrhythmias and ACS-related diseases, including 162,622 12-lead ECGs collected between January 2018 and March 2021. We presented a deep learning model for clinical ECG diagnosis of multiple cardiac diseases. Results show that our model for diagnosing 15 cardiac abnormalities achieved 88.216% accuracy, and its average AUC ROC score reached 0.961. On the board-certified re-annotated dataset, its performance surpasses that of cardiologists in non-reference group. Moreover, with aid of labels given by our model, accuracy and efficiency for cardiologist increased by 13.5% and 69.9% than non-reference group. Our approach provides solutions for AI-aided diagnosis systems of cardiac diseases in applications.

Indexed as

Biological sciencesCardiovascular medicineDiagnostics

Identifiers

PMID36388959
PMCPMC9664363
OpenAlexW4307570120

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.