Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

ABSTRACT:

Background

The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations.

Methods

This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN)

SUBMITTER: Gong Z 

PROVIDER: S-EPMC10722026 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature