College of Digital Innovation Technology, Rangsit University, Pathum Thani, Thailand.
Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
BMC Oral Health. 2024 May 2;24(1):519. doi: 10.1186/s12903-024-04279-6.
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.
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.
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.
The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.
口腔癌是一种致命的疾病，也是全球发病率和死亡率的主要原因。本研究旨在开发一种基于模糊深度学习（FDL）的模型，根据口腔癌的临床病理数据来估计生存时间。
本研究回顾性收集了 2011 年至 2019 年期间在口腔颌面外科诊所和区域癌症中心接受手术联合或不联合放化疗治疗的 581 例口腔鳞状细胞癌（OSCC）患者的电子病历。基于临床病理数据，深度学习（DL）模型被训练用于对生存时间类别进行分类。模糊逻辑被整合到 DL 模型中，并进行训练以创建基于 FDL 的模型来估计生存时间类别。
模型的性能在测试数据集上进行了评估。DL 和 FDL 模型在估计生存时间方面的表现，其准确性分别为 0.74 和 0.97，接受者操作特征（ROC）曲线下面积（AUC）分别为 0.84 到 1.00 和 1.00。
将模糊逻辑集成到 DL 模型中可以提高基于口腔癌临床病理数据估计生存时间的准确性。
