Evaluating AI-assisted detection of fetal intracranial malformations in prenatal ultrasound practice: a multicentre, self-crossover, randomised controlled trial in China

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Abstract

BACKGROUND: The effect of AI-assisted diagnosis on sonographer performance in real-world prenatal settings remains unclear. This study aimed to assess the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS) in detecting AI-recognisable intracranial malformations and its effect on identifying anomalies beyond its recognition scope in high-risk pregnancies. METHODS: This multicentre, self-crossover, randomised controlled trial was done at five Chinese centres. High risk of fetal malformation singleton pregnancies (11-32 weeks' gestation) were examined by sonographers with 3 to less than 8 years of experience. Participants were randomly assigned 1:1 to two diagnostic sequences: independent real-time diagnosis followed by PAICS-assisted offline review, or PAICS-assisted real-time diagnosis followed by independent offline review, with a 4-week washout period. Allocation was concealed. An expert panel's diagnosis on video review served as the reference standard. Sonographers were masked to fetal anomaly status but were aware of AI assistance during scanning. Outcome assessors and the independent expert panel responsible for the reference standard diagnosis were masked to group assignments. Primary outcomes were sensitivity and specificity in detecting ten specific fetal intracranial malformations, with sensitivity assessed for superiority and specificity against a 5% margin of non-inferiority. The trial is registered with the Chinese Clinical Trial Registry (ChiCTR2200063424). FINDINGS: Between Sept 6, 2022 and Nov 1, 2023, 1584 scans were completed. PAICS-assisted diagnosis improved sensitivity for detecting specific fetal intracranial malformations. In the fetal-based analysis, sensitivity increased by 0·087 (95% CI 0·029 to 0·147; psuperiority<0·0001), with specificity meeting the non-inferiority criterion (difference 0·009, 95% CI -0·006 to 0·023; pnon-inferiority<0·0001). In the malformation-targeted analysis, sensitivity improved by 0·118 (95% CI 0·054 to 0·181; psuperiority<0·0001), and specificity was also non-inferior (difference 0·001, 95% CI -0·001 to 0·002; pnon-inferiority<0·0001). No adverse events related to the diagnostic procedure or the AI assistance were reported. INTERPRETATION: PAICS improves sonographers' sensitivity for detecting targeted fetal intracranial malformations while preserving their specificity, which supports the integration of AI assistance into fetal anomaly screening in high-risk clinical settings. FUNDING: National Natural Science Foundation of China, Guangdong Provincial Basic and Applied Basic Research Fund Project, Guangzhou Science and Technology Program, and Sun Yat-sen University Fundamental Research Funds for the Junior Faculty Program.

Abstract as published, via PubMed.