Artificial intelligence (AI)-based multi-organ contour quality assurance with uncertainty estimation for online adaptive radiotherapy (oART)

Machine Learning Health 2026 Open access

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Authors: Shunyu Yan, Jiacheng Xie, Na Chen, Dan T. D. Nguyen, Fan Chi Su, Daniel Xiao Yang, You Zhang, Steve B Jiang, Chenyang Shen
DOI: 10.1088/3049-477x/ae3320
Cited by: 0
References: 43

Abstract

contours. Moreover, incorrect predictions were strongly correlated with high uncertainty scores, underscoring the effectiveness in improving QA reliability. The proposed AI-based framework holds the potential to automate contour QA with quantified uncertainty. It is well-suited for oART with heavy time constraints given its high accuracy, efficiency, and reliability.

Fields of Study & Subjects

Advanced Radiotherapy Techniques Prostate Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Machine Learning Health
Volume
2
Issue
1
Pages
015001–015001
Publication date
5 January 2026
Language
EN