ArticleBMC oral health2024
Survival estimation of oral cancer using fuzzy deep learning.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.BMC cancer · 2026Pooled it
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.BMC public health · 2026Article
- "Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".BMC cancer · 2025Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundOral 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.
methodsElectronic 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.
resultsThe 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.
conclusionsThe integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.