Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.

ABSTRACT:

Objectives

Automating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a first step.

Methods

A convolutional neural network with SqueezeNet architecture was first trained to classify two groups of PRs (91with and 91without impacted canines) on the MATLAB programming platform. Based on results, the need to crop the PRs was realized. Next, artificial intelligence (AI) detectors were trained to identify specific landmarks (maxillary central incisors, lateral incisors, canines, bicuspids, nasal area, and the mandibular ramus) on the PRs. Landmarks were then explored to guide cropping of the PRs. Finally, impro

SUBMITTER: Abdulkreem A 

PROVIDER: S-EPMC11003657 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature