Evidence map›Paper›PMID 37176738›Full record

ReviewJournal of clinical medicine2023

Application and Progress of Artificial Intelligence in Fetal Ultrasound.

Sushan Xiao, Junmin Zhang, Ye Zhu, Zisang Zhang, Haiyan Cao, Mingxing Xie, Li Zhang

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06952439 (Prospective Multicenter Study to Evaluate the Performance of the Artificial Intelligence), which is not on this map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
–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.

NCT06952439 completednot on this mapstarted 2025, after this paper: background citation

Prospective Multicenter Study to Evaluate the Performance of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT

TypeobservationalSponsorOrigin Medical Systems, Inc.Ran2025 to 2025Enrolled551ConditionsTo Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants, First Trimester PregnancyArmsImages and cines captured through the capture card (ICC)
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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.

Sushan XiaoDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Junmin ZhangDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Ye ZhuDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Zisang ZhangDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Haiyan CaoDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Mingxing XieDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.ORCID 0000-0003-4984-1567
Li ZhangDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.ORCID 0000-0003-1620-1512

Funding

Key Research and Development Program of Hubei 2021CFA046National Natural Science Foundation of China 82151316National Natural Science Foundation of China 82171964National Natural Science Foundation of China 82202194
6 · The paper itself

Abstract

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

Indexed as

artificial intelligenceconvolution neural networkdeep learningfetal ultrasoundprenatal diagnosis

Identifiers

PMID37176738
PMCPMC10179567

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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.