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Background <p>Despite 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.</p> Methods <p>We 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 (&lt; 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.</p> Results <p>We analyzed 61,959 examinations (551,144 images) from 186 sonographers at 34 hospitals. Over 36&#xa0;months, the combined proportion of first-trimester and standard anomaly scans increased from 33.1% to 66.8% (<i>p</i> &lt; 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 <i>p</i> &lt; 0.0001). By 18–24&#xa0;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 &lt; 0.0001). At the plane level, representative views showed improvement; for example, standard transthalamic plane images increased from 91 to 97% (p for trend &lt; 0.0001), accompanied by marked reductions in common deficiencies.</p> Conclusions <p>AI-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.</p>

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

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  • 发表日期：

    2026年3月6日

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  • Jianxin Zhao,
  • Yao Tang,
  • Shengli Li,
  • Ke Wang,
  • Jing Tao,
  • Chunyi Chen,
  • Jiayuan Zhou,
  • Lang Cui,
  • Yuji Wang,
  • Cheng Huang,
  • Zheng Liu,
  • Hong Kang,
  • Jun Zhu,
  • Yong Huang

摘要

Background

Despite 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.

Methods

We 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.

Results

We 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.

Conclusions

AI-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.