A Unified Deep Learning Framework for Visual Diagnosis of Palatal Radicular Grooves in CBCT Scans: A Multi-Center Validation Study.
Authors
Affiliations (7)
Affiliations (7)
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China;; Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
- Qingdao Stomatological Hospital Affiliated to Qingdao University, Qingdao, China.
- Department of Endodontics I, Stomatological Hospital of Xiamen Medical College, Xiamen, China; Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment, Xiamen, China.
- Department of International VIP Dental Clinic, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, China; Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction, Tianjin, China.
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China;; Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address: [email protected].
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China;; Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address: [email protected].
Abstract
Palatal radicular grooves (PRGs) posed diagnostic challenges due to their complex root anatomy and subtle manifestations in cone-beam computed tomography (CBCT). This study aimed to develop a deep learning (DL) framework for the automated three-dimensional visualization, diagnosis, and classification of PRG lesions. A unified framework (PRG-Net) integrating tooth segmentation, PRG diagnosis, and lesion classification was developed. A retrospective multicenter diagnostic accuracy study was conducted using CBCT datasets with varying fields of view from one internal validation site and three external centers to evaluate generalizability and performance for segmentation, diagnosis, and classification tasks. The impact of PRG-Net on dentists' diagnostic accuracy, classification consistency, and workflow efficiency was also assessed. PRG-Net demonstrated strong generalizability across all datasets. For tooth segmentation, it achieved a mean Dice Similarity Coefficient (DSC) of 97.1% [95% CI: 96.4, 97.7]. Diagnostic performance yielded an Area Under the Curve (AUC) of 94.4% (internal) and 85.2%-90.0% (external). Classification AUC were 91.4% [95% CI: 86.8, 96.1] for Type I, 88.5% [95% CI:81.1, 95.8] for Type II, and 96.9% [95% CI:91.6, 100] for Type III, with consistent cross-center reproducibility. In clinical validation, PRG-Net significantly improved dentists' diagnostic accuracy and inter-rater classification agreement while substantially reducing interpretation time. PRG-Net provided a robust, automated solution for PRG assessment in CBCT. It facilitated earlier and more precise diagnosis, improved inter-rater reliability, and streamlined workflow, demonstrating strong potential as a clinically valuable decision-support tool to guide treatment planning and improve patient outcomes.