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AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural
Marie Louise Slim1,2, Reinhilde Jacobs3,4, Renata Maíra de Souza Leal1,5
1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Kapucijnenvoer 7, Leuven, 3000, Belgium.
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.
