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A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography
Published on: July 22, 2022
Developing, testing, and validating a clinical decision support tool to improve the quality of intraoral dental
Jay Patel1, Mustafa Badi2, Ritwik Katiyar1
1Center for Artificial Intelligence, Data Science, and Informatics, Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
Objectives:
This study aimed to develop, test, and clinically validate an AI-driven Clinical Decision Support Tool (CDST) to enhance dental radiographic quality, with the goal of reducing retakes and improving diagnosis.
Study Design:
About 188,684 radiographs from 16,703 patients (2020-2024) were manually labeled. Two enhancement strategies were developed: (1) generalized and (2) localized enhancements. For generalized enhancement, two further approaches were developed, including traditional algorithms with the BSRGAN model, and a custom AI pipeline integrating the lighting enhancement-net and enhancement U-net with BSRGAN to remove artifacts, correct exposure, reduce noise, blurriness, and improve sharpness. Localized enhancement involved an ensemble YOLO v8.2 segmentation model followed by LaMa inpainting to remove artifacts. Performance was evaluated on test sets using standard metrics. A panel of dental experts reviewed the original and enhanced radiographs to assess diagnostic quality.
Results:
The YOLO model achieved 92% accuracy, while LaMa achieved 96%. Expert evaluation of 1,500 radiographs indicated that 81% were improved, 15% were unchanged, and 3% showed reduced diagnostic quality, with statistically significant differences (P < .05).
Conclusions:
This AI-driven, expert-validated CDST enhances global and localized radiographic quality, potentially reducing retakes, lowering radiation exposure, and improving diagnostic reliability.
