Exploring a New Paradigm for the Fetal Anomaly Ultrasound Scan: Artificial Intelligence in Real Time

Abstract

Background: Ultrasound (US) imaging is characterised by high levels of operator subjectivity and variability. Recent advances in artificial intelligence (AI) have demonstrated the potential to reduce both factors. This study pilots the end-to-end automation of multiple elements of the mid-trimester obstetric screening US scan using AI-enabled tools. Methods: A single centre, prospective method comparison study was conducted. Participants had both standard manual and AI-assisted US scans performed independently by different blinded sonographers. The AI-tools automated the acquisition of standard plane images, measurement of fetal biometrics and the production of a written clinical report with saved images available for review. A feedback survey captured the sonographers’ perception of scanning with both methods. Findings: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) when using the AI-assisted method (p<0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI-tools saved a satisfactory view in 93% of the cases when considering the four core views only, and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI-tools helped sonographers to concentrate on image interpretation by removing disruptive recording and measurement tasks. Interpretation: Use of AI to automate tasks during the ultrasound examination changes workflow. Separating the process of freehand scanning from image capture and measurement resulted in a faster scan. Removing the need for the sonographer to focus on repetitive tasks may allow more attention to be directed towards identification of atypical fetal anatomy. Further work is required to improve the performance of the image plane detection algorithm for use in real time. In the future, high quality AI-tools could allow the sonographer to increase their focus on anatomical assessment for congenital anomaly detection and provide higher-quality parent-centred care. Trial Registration: This study was performed as part of the intelligent Fetal Imaging and Diagnosis (iFIND) project, NRES number = 14/LO/1806 (ISRCTN = 16542843). Funding: This work was supported by The Wellcome Trust, EPSRC, and NIHR. Declaration of Interests: AG reports consultancy fees from Ultromics Ltd. BK reports consultancy fees from ThinkSono Ltd, Ultromics Ltd. and Cydar Medical Ltd. DR reports consultancy fees from Heartflow, and IXICO, and in addition DR has a patent US20200027237A1 pending. All other authors report no conflict of interests. Ethics Approval Statement: The study has been granted NHS R&D and ethics approval, NRES ref no = 14/LO/1086.