Evidence map›Paper›PMID 41787418›Full record

Observational studyBMC medicine2026

AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.

Jianxin Zhao, Yao Tang, Shengli Li, Ke Wang, Jing Tao, Chunyi Chen, Jiayuan Zhou, Lang Cui, Yuji Wang, Cheng Huang and 4 more

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

14 authors.

Jianxin Zhao *National Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Yao Tang *Prenatal Diagnosis Center in Guizhou Province, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, 550001, China.
Shengli LiDepartment of Ultrasound, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, 518028, China.
Ke WangNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Jing TaoNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Chunyi ChenNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Jiayuan ZhouNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Lang CuiNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Yuji WangNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Cheng HuangNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Zheng LiuNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Hong KangNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.
Jun ZhuNational Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China. zhujun028@163.com.
Yong HuangDepartment of Information Management, West China Second University Hospital, Sichuan University, Sec.1 No.1416 Cheng Long Road, Chengdu, Sichuan, 610066, China. 18394819902@163.com.

Funding

National Key Research and Development Program of China 2022YFC2704701National Key Research and Development Program of China 2024YFC2707000
6 · The paper itself

Abstract

backgroundDespite rapid advances in medical artificial intelligence (AI), robust evidence for real-world clinical application-particularly in low-resource settings (LRS)-remains limited. To address this gap, we conducted a multicenter evaluation of an AI-based quality control (AI-QC) system for fetal ultrasound images across hospitals in Guizhou Province, China.

methodsWe implemented an independent, post-examination AI-QC system in Guizhou. After image uploaded, the system assigns a 0-100 score and classifies images as standard (≥ 80), basic-standard (60-79), or non-standard (< 60). From September 2020 to May 2025, we prospectively collected ultrasound examinations uploaded by sonographers. Examinations were categorized into four types according to national guideline: first-trimester scan (2 planes), basic biometry scan (3 planes), limited anomaly scan (11 planes), and standard anomaly scan (23 planes). First-trimester and standard anomaly scans represent the highest technical demands. Quality was assessed at two levels: examination level (proportion of required images per examination classified as standard; 100% defined as full-standard); and plane level (proportion of images for a given view classified as standard). Primary outcomes were temporal trends in these two measures.

resultsWe analyzed 61,959 examinations (551,144 images) from 186 sonographers at 34 hospitals. Over 36 months, the combined proportion of first-trimester and standard anomaly scans increased from 33.1% to 66.8% (p < 0.0001). The proportion of full-standard examinations increased significantly across all categories: first-trimester scans from 39.5% to 82.1%, basic biometry from 46.3% to 65.5%, limited anomaly from 29.2% to 58.8%, and standard anomaly scans from 16.1% to 53.3% (all p < 0.0001). By 18-24 months post-deployment, most counties surpassed a 60% examination-level standardization threshold; for example, for first-trimester scans, the proportion of counties with mean rates ≥ 60% increased from 31.6% to 68.4% (p for trend < 0.0001). At the plane level, representative views showed improvement; for example, standard transthalamic plane images increased from 91 to 97% (p for trend < 0.0001), accompanied by marked reductions in common deficiencies.

conclusionsAI-based quality control was associated with improved image quality in LRS, with sustained improvements over time. Future studies linking image quality to diagnostic performance and perinatal outcomes are needed to establish clinical benefit.

Indexed as

Artificial IntelligenceQuality ControlUltrasonography, PrenatalChinaFemaleHumansPregnancyProspective StudiesResource-Limited SettingsAI in obstetricsArtificial IntelligenceFetal UltrasoundLow-Resource SettingsQuality Control

Identifiers

PMID41787418
PMCPMC13077844

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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.