AI Article Synopsis
- A study developed and validated an AI tool for automatically segmenting the pulp cavity of mandibular molars from cone-beam CT images, dividing data into training, validation, and testing sets for evaluation.
- The AI tool demonstrated high accuracy in segmentation, with Dice similarity coefficients of 88% for first molars and 90% for second molars, while also significantly reducing time needed for segmentation compared to manual methods.
- This AI-driven method can enhance efficiency in endodontic procedures by providing quick, accurate 3D models, potentially improving patient outcomes and anticipating complications.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582138 | PMC |
| http://dx.doi.org/10.1007/s00784-024-06009-2 | DOI Listing |
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