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BMC Oral Health
Published in: BMC Oral Health 1/2024

Open Access 01-12-2024 | Oral Cancer | Research

Survival estimation of oral cancer using fuzzy deep learning

Authors: Rachasak Somyanonthanakul, Kritsasith Warin, Sitthi Chaowchuen, Suthin Jinaporntham, Wararit Panichkitkosolkul, Siriwan Suebnukarn

Published in: BMC Oral Health | Issue 1/2024

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Abstract

Background

Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer.

Methods

Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes.

Results

The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively.

Conclusions

The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.
Appendix
Available only for authorised users
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Metadata
Title
Survival estimation of oral cancer using fuzzy deep learning
Authors
Rachasak Somyanonthanakul
Kritsasith Warin
Sitthi Chaowchuen
Suthin Jinaporntham
Wararit Panichkitkosolkul
Siriwan Suebnukarn
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2024
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-024-04279-6

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