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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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The AI-driven tool achieved high accuracy, with a Dice similarity coefficient (DSC) of 88% \u00b1 7% for first molars and 90% \u00b1 6% for second molars (<jats:italic>p<\/jats:italic>\u2009&gt;\u2009.05). The 95% Hausdorff distance (HD) was lower for AI-driven segmentation (0.13\u2009\u00b1\u20090.07) compared to manual segmentation (0.21\u2009\u00b1\u20090.08) (<jats:italic>p<\/jats:italic>\u2009&lt;\u2009.05). Regarding time efficiency, AI-driven (4.3\u2009\u00b1\u20092\u00a0s) and R-AI segmentation (139\u2009\u00b1\u200993\u00a0s) methods were the fastest, compared to manual segmentation (2349\u2009\u00b1\u2009444\u00a0s) (<jats:italic>p<\/jats:italic>\u2009&lt;\u2009.05).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The AI-driven segmentation proved to be accurate and time-efficient in segmenting the pulp cavity system in mandibular molars.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical Relevance<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00784-024-06009-2","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T09:04:43Z","timestamp":1732179883000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks"],"prefix":"10.1007","volume":"28","author":[{"given":"Marie Louise","family":"Slim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhilde","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata Ma\u00edra","family":"de Souza Leal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rocharles Cavalcante","family":"Fontenele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"6009_CR1","unstructured":"CORDIS, European Commission (2020) Increasing the success rate of root canals. https:\/\/cordis.europa.eu\/article\/id\/413194-increasing-the-success-rate-of-root-canals. 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