Kayadibi İsmail, Köse Utku, Güraksın Gür Emre, Çetin Bilgün
Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Isparta, Turkey; Department of Management Information Systems, Faculty of Economic and Administrative Sciences, Afyon Kocatepe University, Afyonkarahisar, Turkey.
Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Isparta, Turkey.
Int J Med Inform. 2025 Mar;195:105724. doi: 10.1016/j.ijmedinf.2024.105724. Epub 2024 Nov 23.
This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic radiography (PR). The proposed architecture seeks to enhance the accuracy of early detection and improve clinical decision-making and treatment planning in dentistry.
A new dataset, named the Mandibular Third Molar (m-TM) dataset, was developed through expert labeling of raw PR images from the UESB dataset. This dataset was subsequently made publicly accessible to support further research. Several advanced image preprocessing techniques, including Gaussian filtering, gamma correction, and data augmentation, were applied to improve image quality. Various Deep learning (DL) based Convolutional Neural Network (CNN) architectures were trained and validated using Transfer Learning (TL) methodologies. Among these, the E-mTMCNN, leveraging the GoogLeNet architecture, achieved the highest performance metrics. To ensure transparency in the model's decision-making process, Local Interpretable Model-Agnostic Explanations (LIME) were integrated as an eXplainable Artificial Intelligence (XAI) approach. Clinical reliability and applicability were assessed through an expert survey conducted among specialized dentists using a decision support system based on the E-mTMCNN.
The E-mTMCNN architecture demonstrated a classification accuracy of 87.02%, with a sensitivity of 75%, specificity of 94.73%, precision of 77.68%, an F1 score of 75.51%, and an area under the curve (AUC) of 87.01%. The integration of LIME provided visual explanations of the model's decision-making rationale, reinforcing the robustness of the proposed architecture. Results from the expert survey indicated high clinical acceptance and confidence in the reliability of the system.
The findings demonstrate that the E-mTMCNN architecture effectively detects the presence of m-M3 in PRs, outperforming current state-of-the-art methodologies. The proposed architecture shows considerable potential for integration into computer-aided diagnostic systems, advancing early detection capabilities and enhancing the precision of treatment planning in dental practice.
本研究旨在设计并系统评估一种架构，即可解释下颌第三磨牙卷积神经网络（E-mTMCNN），用于在全景X线摄影（PR）中检测下颌第三磨牙（m-M3）的存在。所提出的架构旨在提高早期检测的准确性，并改善牙科临床决策和治疗计划。
通过对来自UESB数据集的原始PR图像进行专家标注，开发了一个名为下颌第三磨牙（m-TM）数据集。该数据集随后公开提供以支持进一步研究。应用了几种先进的图像预处理技术，包括高斯滤波、伽马校正和数据增强，以提高图像质量。使用迁移学习（TL）方法对各种基于深度学习（DL）的卷积神经网络（CNN）架构进行训练和验证。其中，利用GoogLeNet架构的E-mTMCNN取得了最高的性能指标。为确保模型决策过程的透明度，集成了局部可解释模型无关解释（LIME）作为一种可解释人工智能（XAI）方法。通过使用基于E-mTMCNN的决策支持系统，在专业牙医中进行专家调查，评估临床可靠性和适用性。
E-mTMCNN架构的分类准确率为87.02%，灵敏度为75%，特异性为94.73%，精确率为77.68%，F1分数为75.51%，曲线下面积（AUC）为87.01%。LIME的集成提供了模型决策原理的可视化解释，增强了所提出架构的稳健性。专家调查结果表明临床对该系统的接受度高且对其可靠性有信心。
研究结果表明，E-mTMCNN架构能有效检测PR中m-M3的存在，优于当前的最先进方法。所提出的架构在集成到计算机辅助诊断系统方面显示出相当大的潜力，可提高早期检测能力并增强牙科实践中治疗计划的精度。
