Article

Development and Clinical Validation of an Enhanced Deep Learning Model for Automated Segmentation and Implant Planning in Maxillary Edentulous Regions Using CBCT Images

2026-07-06

Abstract excerpt

<title>Abstract</title> <p>Objectives This study aimed to develop and clinically validate an advanced deep learning framework for automated segmentation of maxillary edentulous regions on cone-beam computed tomography (CBCT) images and to evaluate its role in supporting digital implant planning workflows. Materials and Methods A total of 450 CBCT scans were retrospectively screened, and 320 scans with partial m...

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Literature Corpus work: 1b0b5171-22a3-59ee-aa81-351aecdbd380
DOI: 10.21203/rs.3.rs-10084556/v1

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