PuSH - Publikationsserver des Helmholtz Zentrums München

Matthew, J.* ; Skelton, E.* ; Day, T.G.* ; Zimmer, V.A.* ; Gomez, A.* ; Wheeler, G.* ; Toussaint, N.* ; Liu, T.* ; Budd, S.* ; Lloyd, K.* ; Wright, R.* ; Deng, S.* ; Ghavami, N.* ; Sinclair, M.* ; Meng, Q.* ; Kainz, B.* ; Schnabel, J.A.* ; Rueckert, D.* ; Razavi, R.* ; Simpson, J.A.* ; Hajnal, J.*

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

Prenat. Diagn. 42, 49-59 (2022)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Objective: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools. Methods: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. Results: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with 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 (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 tasks. Conclusion: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0197-3851
e-ISSN 1097-0223
Zeitschrift Prenatal Diagnosis
Quellenangaben Band: 42, Heft: 1, Seiten: 49-59 Artikelnummer: , Supplement: ,
Verlag Wiley
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)