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Automated Trabecular Bone Classification by nazile.fernanda is a document available to read on EtoBox.
This study developed and validated an automated classification method for trabecular bone patterns at dental implant sites using 3D morphometric parameters derived from CBCT images. The automated method achieved an overall classification accuracy of 84%, outperforming subjective classifications by oral radiologists, which had a fair interobserver agreement. The findings suggest that computer-aided assessment could enhance objectivity in evaluating bone quality prior to implant placement.
- Author
- nazile.fernanda
- Language
- EN