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A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma
- Source :
- iScience, Vol 26, Iss 12, Pp 108347- (2023)
- Publication Year :
- 2023
- Publisher :
- Elsevier, 2023.
-
Abstract
- Summary: It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p
- Subjects :
- Health technology
Applied computing
Science
Subjects
Details
- Language :
- English
- ISSN :
- 25890042
- Volume :
- 26
- Issue :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- iScience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b4270ef9ccac4c828f68fe93491a5b13
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.isci.2023.108347