ArticleBMC pregnancy and childbirth2025
Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- A Robotic Ultrasound System for Automated Abdominal Aorta Screening: Feasibility Study in Healthy Volunteers.Sensors (Basel, Switzerland) · 2026Article
- A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions.Sensors (Basel, Switzerland) · 2026Review
- Beyond Computer-Aided Diagnosis: Artificial Intelligence as a "Digital Mentor" for POCUS Image Acquisition and Quality Assurance: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- Intelligent quality control for first trimester ultrasound scans in Liaoning Province of China.NPJ digital medicine · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundRegular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI-IQA) system to audit images from the four key planes used in first-trimester scanning.
methodsThe AI-IQA system was developed based on the YOLOv7 structure detection network and a multi-branch image quality regression network using a large multicenter internal dataset. Clinical validation was performed using 567 cases scanned by four radiologists with different experience levels, of which 349 were performed without AI-IQA feedback (clinical test set 1) and 218 were performed after 2-3 rounds of AI-IQA feedback (clinical test set 2). The proportion of standard images obtained and detailed expert audit results were compared to verify whether AI-IQA could objectively and accurately provide feedback on deficiencies in nonstandard images to assist radiologists at different experience levels in improving image quality.
resultsIn the internal test set, the AI-IQA system achieved high average accuracy precision, recall and F1-score in auditing the overall plane quality (0.881, 0.833, 0.842 and 0.837, respectively) and structure quality (0.906, 0.861, 0.857 and 0.859, respectively). In clinical test sets 1 and 2, AI-IQA results showed strong consistency with expert assessment results, with the average Cohen's Kappa coefficient exceeding 0.8 for all four planes. In addition, following AI-IQA feedback, the proportion of standard images obtained by junior and mid-level radiologists increased by 7.7% and 5.1%, respectively. AI-IQA takes only 0.05 s to assess each image, while experts require more than 20 s (p < 0.001).
conclusionsThe proposed AI-IQA system proved to be a highly accurate and efficient method of automatically auditing first-trimester scanning image quality, providing precise and rapid key plane quality control. This tool can also assist radiologists with different levels of experience to improve the image quality.
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