Artificial intelligence assisted multitask framework for dental detection and root canal filling assessment on...

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.

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.

The proposed image enhancement significantly improved model performance, with the total mAP 50 increasing from 92.3% to 99.6%. The model achieved excellent diagnostic reliability, with intraclass correlation coefficients exceeding 0.96 across all tasks, notably reaching 0.997 for primary teeth. In clinical validation, the AI-assisted framework demonstrated high diagnostic agreement with the consensus of four dentists in classifying RCFL as adequate or underfilled. Furthermore, the AI-calculated RCFR for permanent (73.96%) and primary teeth (77.44%) showed high consistency with expert observations, while AI confidence levels consistently exceeded 95%.

The integrated YOLO11 framework provides a highly accurate, objective, and automated system for the quantitative evaluation of root canal obturation. By delivering reliable second opinions and precise apical limit localization, this system has strong potential to enhance diagnostic consistency and efficiency in clinical endodontic practice.