Journal of dental sciences
Artificial intelligence assisted multitask framework for dental detection and root canal filling assessment on periapical radiographs
By Hsu CL, Ou-Yang LW, Chang CH and 8 others.
Abstract
Background/purpose
Accurate evaluation of root canal filling (RCF) quality is paramount for successful endodontic outcomes. However, conventional radiographic interpretation is often subject to subjective variability. This study developed an automated, multitask artificial intelligence (AI) framework using YOLO11 and advanced image pre-processing to identify multiple dental structures and provide quantitative assessments of RCF quality on periapical radiographs.
Materials and methods
We proposed an image enhancement pipeline, which integrate intensity normalization, white top-hat transform, and linear contrast stretching to refine dental contours for training the YOLO11 multitask model. The framework simultaneously detected primary, permanent, dental crowns, and RCFs. Quantitative metrics, including root canal filling length (RCFL) and filling rate (RCFR), were calculated using pixel-accumulation and landmark localization algorithms.