Evidence map›Paper›PMID 36733613›Full record

ArticleComputational and mathematical methods in medicine2023

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.

Huiling Wu, Bingzheng Wu, Fangping Lai, Peizhong Liu, Guorong Lyu, Shaozheng He, Jiangfeng Dai

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. AI-Driven Analysis of the Fetal Left Ventricular Outflow Tract: Diagnostic Value and Applications.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026
    Article
  2. Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Article
  3. Review
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  9. Evolution of Fetal Cardiac Imaging over the Last 20 Years.Diagnostics (Basel, Switzerland) · 2023
    Review
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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

7 authors at 2 institutions in 1 country.

Huiling WuDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Bingzheng WuCollege of Engineering, Huaqiao University, Quanzhou 362021, China.
Fangping LaiDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Peizhong LiuCollege of Engineering, Huaqiao University, Quanzhou 362021, China.ORCID https://orcid.org/0000-0003-2619-1629
Guorong LyuDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.ORCID https://orcid.org/0000-0003-3123-1138
Shaozheng HeDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.ORCID https://orcid.org/0000-0002-3065-9354
Jiangfeng DaiDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Fujian Medical University · CNHuaqiao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the prognosis of the fetus to a certain extent. At present, prenatal diagnosis of CHD mainly uses 2D ultrasound to directly evaluate the development and function of fetal heart and main structures in the second trimester of pregnancy. Artificial recognition of fetal heart 2D ultrasound is a highly complex and tedious task, which requires a long period of prenatal training and practical experience. Compared with manual scanning, computer automatic identification and classification can significantly save time, ensure efficiency, and improve the accuracy of diagnosis. In this paper, an effective artificial intelligence recognition model is established by combining ultrasound images with artificial intelligence technology to assist ultrasound doctors in prenatal ultrasound fetal heart standard section recognition. The method data in this paper were obtained from the Second Affiliated Hospital of Fujian Medical University. The fetal apical four-chamber heart section, three vessel catheter section, three vessel trachea section, right ventricular outflow tract section, and left ventricular outflow tract section were collected at 20-24 weeks of gestation. 2687 image data were used for model establishment, and 673 image data were used for model validation. The experiment shows that the map value of this method in identifying different anatomical structures reaches 94.30%, the average accuracy rate reaches 94.60%, the average recall rate reaches 91.0%, and the average F1 coefficient reaches 93.40%. The experimental results show that this method can effectively identify the anatomical structures of different fetal heart sections and judge the standard sections according to these anatomical structures, which can provide an auxiliary diagnostic basis for ultrasound doctors to scan and lay a solid foundation for the diagnosis of congenital heart disease.

Indexed as

Artificial IntelligenceHeart Defects, CongenitalEchocardiographyFemaleFetal HeartHumansPregnancyUltrasonography, Prenatal

Identifiers

PMID36733613
PMCPMC9889146
OpenAlexW4317840203

What OpenQuestion holds

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LicenceCC BY
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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.