A Unified Deep Learning Framework for Visual Diagnosis of Palatal Radicular Grooves in CBCT Scans: A Multicenter Validation Study

Qikui Zhu; Weitao Fu; Yeyu Lin; Jiaxing Li; Wenhui Tang; Ying Zhang; Rui Zhang; Guanfan Lu; Yao Lin; Jing Shen; Zhuan Bian; Liuyan Meng

doi:10.1016/j.joen.2026.01.022

A Unified Deep Learning Framework for Visual Diagnosis of Palatal Radicular Grooves in CBCT Scans: A Multicenter Validation Study

J Endod. 2026 Feb 6:S0099-2399(26)00031-2. doi: 10.1016/j.joen.2026.01.022. Online ahead of print.

Authors

Qikui Zhu¹, Weitao Fu², Yeyu Lin², Jiaxing Li², Wenhui Tang², Ying Zhang², Rui Zhang², Guanfan Lu³, Yao Lin⁴, Jing Shen⁵, Zhuan Bian⁶, Liuyan Meng⁷

Affiliations

¹ 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.
² 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.
³ Qingdao Stomatological Hospital Affiliated to Qingdao University, Qingdao, China.
⁴ Department of Endodontics I, Stomatological Hospital of Xiamen Medical College, Xiamen, China; Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment, Xiamen, China.
⁵ 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.
⁶ 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: bianzhuan@whu.edu.cn.
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; Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address: mengliuyan@whu.edu.cn.

PMID: 41655629
DOI: 10.1016/j.joen.2026.01.022

Abstract

Introduction: 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 framework for the automated three-dimensional visualization, diagnosis, and classification of PRG lesions.

Methods: 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 3 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.

Results: PRG-Net demonstrated strong generalizability across all datasets. For tooth segmentation, it achieved a mean Dice similarity coefficient of 97.1% [95% CI: 96.4, 97.7]. Diagnostic performance yielded area under the curve of 94.4% (internal) and 85.2%-90.0% (external). Classification area under the curve 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.

Conclusions: 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.

Keywords: Artificial intelligence; PRG; cone-beam computed tomography; diagnosis; endodontics.