Evidence map›Paper›PMID 39493884›Full record

ArticleiScience2024

Artificial intelligence diagnosis of patent foramen ovale in contrast transthoracic echocardiography.

Yuanyuan Sheng, Lixin Chen, Mengjie Gu, Shuyu Luo, Yuxiang Huang, Xiaoxuan Lin, Xiaohua Liu, Qian Liu, Xiaofang Zhong, Guijuan Peng and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

16 authors.

Yuanyuan ShengShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Lixin ChenShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Mengjie GuSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Shuyu LuoShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Yuxiang HuangShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Xiaoxuan LinShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Xiaohua LiuShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Qian LiuShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Xiaofang ZhongShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Guijuan PengShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Jian LiShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Bobo ShiShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Lin WangSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Jinfeng XuShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.
Zhaohui NingThe First Afffiliated Hospital, Henan University of Science and Technology, Luoyang 471003, China.
Yingying LiuShenzhen Medical Ultrasound Engineering Center, Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is rarely directly used in patent foramen ovale (PFO) diagnosis. In this study, an AI model was developed to detect the presence of PFO automatically in both contrast transthoracic echocardiography (cTTE) images and videos. The whole intelligent diagnosis neural network framework consists of two functional modules of image segmentation (Unet,

Indexed as

Artificial intelligenceComputer-aided diagnosis methodHealth sciences

Identifiers

PMID39493884
PMCPMC11530856

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.