Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Matthew:2021:10.1002/pd.6059,
author = {Matthew, J and Skelton, E and Day, TG and Zimmer, VA and Gomez, A and Wheeler, G and Toussaint, N and Liu, T and Budd, S and Lloyd, K and Wright, R and Deng, S and Ghavami, N and Sinclair, M and Meng, Q and Kainz, B and Schnabel, JA and Rueckert, D and Razavi, R and Simpson, J and Hajnal, J},
doi = {10.1002/pd.6059},
journal = {Prenatal Diagnosis},
pages = {49--59},
title = {Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time},
url = {http://dx.doi.org/10.1002/pd.6059},
volume = {42},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - ObjectiveAdvances 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.MethodsA 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.ResultsTwenty-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.ConclusionSeparating 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.
AU  - Matthew,J
AU  - Skelton,E
AU  - Day,TG
AU  - Zimmer,VA
AU  - Gomez,A
AU  - Wheeler,G
AU  - Toussaint,N
AU  - Liu,T
AU  - Budd,S
AU  - Lloyd,K
AU  - Wright,R
AU  - Deng,S
AU  - Ghavami,N
AU  - Sinclair,M
AU  - Meng,Q
AU  - Kainz,B
AU  - Schnabel,JA
AU  - Rueckert,D
AU  - Razavi,R
AU  - Simpson,J
AU  - Hajnal,J
DO  - 10.1002/pd.6059
EP  - 59
PY  - 2021///
SN  - 0197-3851
SP  - 49
TI  - Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time
T2  - Prenatal Diagnosis
UR  - http://dx.doi.org/10.1002/pd.6059
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000708287900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://obgyn.onlinelibrary.wiley.com/doi/10.1002/pd.6059
UR  - http://hdl.handle.net/10044/1/96818
VL  - 42
ER  -
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