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It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The mean DSC and IoU between DL and SA segmentations were 0.96\u2009\u00b1\u20090.01 and 0.92\u2009\u00b1\u20090.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62\u00a0mm\u2009\u00b1\u20090.16\u00a0mm and 0.77\u00a0mm\u2009\u00b1\u20090.31\u00a0mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85\u2009\u00b1\u20090.08; 0.78\u2009\u00b1\u20090.07 and 0.91\u2009\u00b1\u20090.92\u00a0mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model\u2019s robustness.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Clinical relevance<\/jats:title>\n            <jats:p>Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00784-024-06136-w","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T11:50:49Z","timestamp":1736769049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation"],"prefix":"10.1007","volume":"29","author":[{"given":"Daniel","family":"Palkovics","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Hegyi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balint","family":"Molnar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Frater","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Csaba","family":"Pinter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Garc\u00eda-Mato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andres","family":"Diaz-Pinto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Windisch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"6136_CR1","volume-title":"The Handbook of Artificial Intelligence: Volume 1","author":"A Barr","year":"2014","unstructured":"Barr A, Feigenbaum EA (2014) The Handbook of Artificial Intelligence: Volume 1. 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