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Updated: Jul 18, 2026
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
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Deep learning in oral cancer- a systematic review
Kritsasith Warin1, Siriwan Suebnukarn2
1Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand. warin@tu.ac.th.
BMC Oral Health
|February 10, 2024
Summary
Deep learning (DL) shows significant promise for oral cancer diagnosis and prognosis. This review highlights DL models achieving high accuracy, aiding clinical decisions for this life-threatening disease.
Area of Science:
- Medical Imaging Analysis
- Computational Pathology
- Oncology Informatics
Background:
- Oral cancer poses a significant threat to patient survival and quality of life.
- Deep learning (DL) applications in oral cancer diagnosis and prognostic prediction are rapidly evolving.
- This systematic review synthesizes current research on DL in oral cancer.
Approach:
- Systematic review adhering to PRISMA guidelines.
- Comprehensive search of Medline (PubMed), Google Scholar, and Scopus databases (January 2000 - June 2023).
- Inclusion of 54 studies focusing on diagnostic and prognostic prediction using DL.
Key Points:
- DL models demonstrated high performance metrics: accuracy (85.0-100%), F1-score (79.31-89.0%), Dice coefficient (76.0-96.3%), and concordance index (0.78-0.95).
- Thirteen studies exhibited low risk of bias across all domains, with 40 showing low concerns regarding applicability.
- Pooled diagnostic odds ratio for classification studies was 2549.08.
Conclusions:
- The volume of DL research in oral cancer is growing, featuring diverse architectures.
- Promising DL performance suggests potential for enhancing clinical decision-making in oral cancer management.
- Further research can refine DL applications for improved patient outcomes in oral oncology.
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