Quantum-Inspired Explainable Deep Learning Framework for Early Enamel Caries Classification in Intraoral Photographs

Early detection of enamel caries is crucial for preventive dentistry but remains challenging due to the subtle and subjective nature of visual examination. This study aims to develop and validate a quantum-inspired, explainable deep learning framework for the automated and interpretable classification of enamel caries from intraoral photographs. This study proposed a hybrid framework utilizing two deep learning models: a custom lightweight CNN named DentXCaries and a fine-tuned ResNet50 with squeeze-and-excitation attention. A novel quantum entanglement feature fusion technique was introduced to combine the deep features from both models. The fused features were classified using twelve machine learning classifiers. The model was developed on a public dataset of 2,000 intraoral images categorized into Early-Stage Enamel Caries, Advanced Enamel Caries, and No Enamel Caries. Explainable AI (Grad-CAM) provided visual explanations for predictions. Performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and statistical tests. The QNN-Caries framework demonstrates state-of-the-art accuracy for enamel caries classification while providing crucial visual interpretability. It represents a significant step towards a reliable, transparent, and clinically viable AI-assisted diagnostic tool for routine dental screenings.牙釉质龋的早期检测对预防牙医学至关重要，但受限于目视检查的细微性与主观性，该任务仍颇具挑战。本研究旨在开发并验证一种量子启发式可解释深度学习框架，以实现基于口内照片的牙釉质龋自动化、可解释分类。本研究提出了一种融合双深度学习模型的混合框架：一是命名为DentXCaries的自定义轻量级卷积神经网络（Convolutional Neural Network, CNN），二是搭载挤压激励（Squeeze-and-Excitation, SE）注意力机制的微调版ResNet50。本研究引入了一种新颖的量子纠缠特征融合技术，用于融合两个模型提取的深度特征。融合后的特征通过十二种机器学习分类器完成了分类。本模型基于包含2000张口内图像的公开数据集开发，图像被划分为早期牙釉质龋、进展期牙釉质龋及无牙釉质龋三类。可解释人工智能（Grad-CAM）可为模型预测结果提供可视化解释。模型性能通过准确率、精确率、召回率、F1值、受试者工作特征曲线下面积（Receiver Operating Characteristic Area Under the Curve, ROC-AUC）及统计学检验进行评估。QNN-Caries框架在牙釉质龋分类任务中展现出当前最优的准确率，同时具备关键的可视化可解释性。该框架为开发用于常规牙科筛查的可靠、透明且具备临床实用性的AI辅助诊断工具迈出了重要一步。