Trial reportBMC medical education2024
Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.
Trial report in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed.
- Automated interpretation of fetal cardiac function evaluation from the echocardiogram.NPJ digital medicine · 2026Article
- AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.BMC medicine · 2026Observational
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Proposing a machine learning-based model for predicting nonreassuring fetal heart.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeThis study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC).
methodsEligible ultrasonographers and pregnant volunteers were prospectively recruited from the Hunan Maternal and Child Health Hospital in May 2023. The ultrasonographers were randomly and evenly assigned to either the AI or Manual QC groups with baseline scores determined in June-July. From August to October, these groups received real-time AI or post-scan manual QC with post-interventional scores recorded monthly. We applied the repeated measures analysis of variance to analyze the between-subject and within-subject effectiveness and time trends in effectiveness (QC score improvement) assessment. An extra 50 pregnant volunteers underwent real-time manual QC, with their screening images utilized for post-scan AI and manual QC. The time cost of real-time AI QC was zero since it only required trainees' involvement. We used Friedman's M and Quade tests to compare multiple independent medians in cost assessment.
resultsThis study recruited 14 ultrasonographers, equally divided into the AI and Manual QC groups. No significant difference existed between the groups concerning age, service year in perinatal diagnosis, male proportion, and QC frequency. The simple effect of the group revealed that the AI QC method outperformed the Manual QC method at least once (F = 13.113, P = 0.004, η
conclusionsThe AI QC method, more cost-effective than the manual method, shows great potential for application in image QC scenarios. The AI QC can enhance operators' skills in perinatal ultrasound screening, while the manual method can only maintain the existing level.
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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.