Abstract
The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588) and validation (n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies.
Highlights
The digital revolution in dentistry has largely automated the conventional dental workflow and drastically reshaped the field
We developed and validated the first Artificial Intelligence (AI)-model for automated tooth angulation measurement on panoramic radiographs, in order to predict wisdom tooth eruption potential in adolescent patients
Panoramic radiographs were retrospectively selected from the Department of Orthodontics
Summary
The digital revolution in dentistry has largely automated the conventional dental workflow and drastically reshaped the field. Digital innovations have smoothened and accelerated the daily practice and created an important ease-of-use in different areas, resulting in a significant reduction of work time and costs. The introduction of cone-beam computed tomography (CBCT). Allowed cross-sectional imaging with limited radiation dose [1]. The availability of three-dimensional (3D) imaging data has led to tremendous progress in terms of clinical accuracies and optimization of diagnosis and treatment planning. The use of intraoral scanners and computer-aided systems (computer-aided design/computer-aided manufacturing CAD/CAM) enabled digitized. Res. Public Health 2020, 17, 3716; doi:10.3390/ijerph17103716 www.mdpi.com/journal/ijerph
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Environmental Research and Public Health
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
