Advanced AI techniques for root disease classification in dental X-rays using deep learning and metaheuristic approach

Abstract

Root dental diseases remain among the most diagnostically challenging conditions in oral healthcare, often leading to treatment delays and suboptimal outcomes. This study is motivated by the limitations of existing automated diagnostic systems, which tend to focus on superficial abnormalities and overlook complex root pathologies such as pulpal infections, periapical lesions, and progressive periodontitis. To bridge this critical gap, we propose an advanced AI-based classification model that integrates ensemble deep learning architectures with a hybrid metaheuristic optimization strategy-namely, the non-population-based Artificial Multiple Intelligence System (np-AMIS) for image augmentation and the population-based AMIS (pop-AMIS) for adaptive decision fusion. This dual-phase approach enhances feature diversity, classification robustness, and computational efficiency. The model was trained and validated on two proprietary datasets, TD-1 and TD-2, achieving classification accuracies of 98.87 % and 98.41 %, respectively. It was further implemented in a real-world application via the Automated Teeth Disease and Abnormality Classification System (A-TD-A-CS), demonstrating 98.95 % accuracy, a rapid response time of 1.5 s, and a System Usability Scale (SUS) score of 94.5 from dental professionals. The system's ability to accurately identify multiple root disease categories highlights its clinical viability and transformative potential. In addition to its current performance, this study lays the groundwork for future extensions to multi-center datasets and cross-modality diagnostics using cone-beam CT or intraoral scans, further advancing intelligent dental care.