aceDeep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review
Introduction Cervical cancer management relies heavily on magnetic resonance imaging (MRI) for staging and treatment planning; however, manual segmentation is time-consuming and prone to variability. This systematic review aimed to evaluate the performance and clinical potential of machine learning (ML) and deep learning (DL) algorithms for automated cervical cancer segmentation on MRI. Methods A systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (PROSPERO…
Deep learning models, especially U-Net variants, achieved Dice scores up to 0.93 for automated segmentation of cervical tumors and related structures on MRI, suggesting improved efficiency and reproducibility over manual segmentation.
Current studies show some concerns for bias and low certainty of evidence due to heterogeneity, limited external validation, and variability by target structure and imaging protocol, requiring further multicenter standardized studies before clinical implementation.
Evidence certainty is limited by heterogeneity across imaging protocols and targets, methodological limitations, and lack of external validation, preventing routine clinical use.
Evidence
- Peer-reviewedJournal of Medical Radiation Sciences2026-09-27
How should this claim be treated?
Truvace Impact Record TRV-2026-1219, v1: “Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.” Truvace, 2026-09-30. /record/TRV-2026-1219 (accessed at citation time). sha256 ae4cc6cff8deed68…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1219 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.