Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT

20260 citationsJournal Articlegold Open Access

Authors

Hanxiao Huang · MEI Technologies (United States)

Bin Zhang · MEI Technologies (United States)

Abstract

The deep learning model enables precise localization and quantitative measurement of fenestration and dehiscence in CBCT images. This approach enhances diagnostic efficiency and accuracy in detecting fenestration and dehiscence, facilitating preoperative orthodontic risk assessment and personalized treatment planning.

Topics & Keywords

Dental Radiography and Imaging Dental Implant Techniques and Outcomes Dental Research and COVID-19

Publication Details

Published in: Frontiers in Bioengineering and Biotechnology

Volume 14, pp. 1752350-1752350

DOI: 10.3389/fbioe.2026.1752350

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Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT

Authors

Abstract

Topics & Keywords

Publication Details