Use of Convolutional Neural Networks for Detection of Pathologies in Dental X-Ray Images in Clinical Decision Support Systems

Abstract

Relevance: The relevance of the study is determined by the need for automated, scalable solutions for processing large volumes of dental radiological images, which provide precise segmentation, detection, and classification of pathologies in the integrated Clinical Decision Support System (CDSS) modules. Aim: The aim of the study is to develop, optimize, and verify a HITL-CDSS framework for dental radiology with multi-level integration of Convolutional Neural Network (CNN) models, ensuring architectural consistency, metric validity, and expert adaptability. Methods: Research methods: critical architectural and functional analysis of CNN models, metric and indicator modelling of efficiency, synthesis and Unified Modelling Language-based (UML)modelling of the CDSS framework, UML optimization with Human-in-the-loop (HITL) integration, metric and indicator verification of HITL-CDSS. Results: Architectural and functional, metric and indicator, as well as UML modelling of CNN architectures was carried out for the purpose of integration into the dental radiology CDSS. The resultant HITL-optimized framework based on DenseNet/EfficientNet, HRNet, YOLOv8 provided AUC = 0.96–0.98, F1@t = 0.91–0.94, DSC = 0.89–0.92, mAP = 0.72–0.77 at ECE = 0.02–0.04. Integration of HITL mechanisms increased Explainable Artificial Intelligence (XAI) interpretability, resistance to domain shifting, and clinical validity, indicating the appropriateness of multi-modular construction of CDSS with the inclusion of expert feedback. Conclusion: The academic novelty of the study is the development of a HITL-CDSS framework with multi-level CNN integration, which provides metrically verified interpretability, domain-stable generalizability, and clinical relevance in dental radiology tasks.

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