Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, R. Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera‐Paredes, Christopher Kelly, Alan Karthikesalingam, Carlton Chu, Dawn Carnell, C.S. Boon, D. D’Souza, Syed Moinuddin, Bethany Garie, Yasmin McQuinlan, Sarah Ireland, Kiarna Hampton, Krystle Fuller, Hugh Montgomery, Geraint Rees, Mustafa Suleyman, Trevor Back, Cían Hughes, Joseph R. Ledsam, Olaf Ronneberger

Journal of Medical Internet Research · 2021

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Summary

Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Subject
Other / interdisciplinary
Source type
Peer-reviewed study
System type
Other
DOI
10.2196/26151
Catalogue ID
BFmoc27vh9-54ci13
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