1. AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks.
- Author
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Slim ML, Jacobs R, de Souza Leal RM, and Fontenele RC
- Subjects
- Humans, Imaging, Three-Dimensional methods, Cone-Beam Computed Tomography methods, Molar diagnostic imaging, Mandible diagnostic imaging, Neural Networks, Computer, Dental Pulp Cavity diagnostic imaging, Artificial Intelligence
- Abstract
Objective: To develop and validate an artificial intelligence (AI)-driven tool for automated segmentation of the pulp cavity system of mandibular molars on cone-beam computed tomography (CBCT) images., Materials and Methods: After ethical approval, 66 CBCT scans were retrieved from a hospital database and divided into training (n = 26, 86 molars), validation (n = 7, 20 molars), and testing (n = 33, 60 molars) sets. After automated segmentation, an expert evaluated the quality of the AI-driven segmentations. The expert then refined any under- or over-segmentation to produce refined-AI (R-AI) segmentations. The AI and R-AI 3D models were compared to assess the accuracy. 30% of the testing sample was randomly selected to assess accuracy metrics and conduct time analysis., Results: The AI-driven tool achieved high accuracy, with a Dice similarity coefficient (DSC) of 88% ± 7% for first molars and 90% ± 6% for second molars (p > .05). The 95% Hausdorff distance (HD) was lower for AI-driven segmentation (0.13 ± 0.07) compared to manual segmentation (0.21 ± 0.08) (p < .05). Regarding time efficiency, AI-driven (4.3 ± 2 s) and R-AI segmentation (139 ± 93 s) methods were the fastest, compared to manual segmentation (2349 ± 444 s) (p < .05)., Conclusion: The AI-driven segmentation proved to be accurate and time-efficient in segmenting the pulp cavity system in mandibular molars., Clinical Relevance: Automated segmentation of the pulp cavity system may result in a fast and accurate 3D model, facilitating minimal-invasive endodontics and leading to higher efficiency of the endodontic workflow, enabling anticipation of complications., Competing Interests: Declarations. Ethical approval: This study was conducted following local ethics committee approval (protocol number S67798) and in compliance with the World Medical Association Declaration of Helsinki on medical research. All patient data were anonymized. Informed consent: For this study type, formal consent is not required. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no conflict of interest., (© 2024. The Author(s).)
- Published
- 2024
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