Evidence map›Paper›PMID 37470945›Full record

ArticleInterdisciplinary sciences, computational life sciences2023

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

Zeye Liu, Hang Li, Wenchao Li, Fengwen Zhang, Wenbin Ouyang, Shouzheng Wang, Aihua Zhi, Xiangbin Pan

Abstract read
PubMed Publisher
In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

8 authors at 3 institutions in 1 country.

Zeye LiuDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Hang LiDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Wenchao LiPediatric Cardiac Surgery, Henan Provincial People's Hospital, Huazhong Fuwai Hospital, Zhengzhou University People's Hospital, Zhengzhou, 450000, China.
Fengwen ZhangDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Wenbin OuyangDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Shouzheng WangDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Aihua ZhiDepartment of Medical Imaging, Fuwai Yunnan Cardiovascular Hospital, Kunming, 650000, China.
Xiangbin PanDepartment of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China. panxiangbin@fuwaihospital.org.ORCID http://orcid.org/0009-0008-5304-3043
Chinese Academy of Medical Sciences & Peking Union Medical College · CNFuwai Yunnan Cardiovascular Hospital · CNZhengzhou University · CN

Funding

Beijing Municipal Science and Technology Project Z201100005420030CAMS Innovation Fund for Medical Sciences 2021-I2M-1-065National high level talents special support plan 2020-RSW02National Natural Science Foundation of China 81970444Sanming Project of Medicine in Shenzhen SZSM202011013The Fundamental Research Funds for the Central Universities 2019PT350005
6 · The paper itself

Abstract

purposeStudies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities.

methodsThe Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m

resultsThe deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8.

conclusionsA deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.

Indexed as

AlgorithmsDeep LearningHeart VentriclesAdultDetection AlgorithmsFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerArtificial intelligenceDeep learningHeart failureMagnetic resonance imagingRight ventricular abnormalities

Identifiers

PMID37470945
OpenAlexW4384818965

What OpenQuestion holds

Textmetadata
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.