Dental Crowding Categorization Network (DCC-Net): Explainable deep learning system for automatic categorization of dental crowding on intraoral photographs.

ABSTRACT:

Objective

Since categorization of dental crowding is a crucial parameter in orthodontic diagnosis and tooth-extraction decisions, we aimed to develop an automatic system to categorize crowding levels on intraoral photographs without space analysis.

Methods

The Dental Crowding Categorization Network (DCC-Net), consisting of segmentation, extraction, and categorization modules, was proposed and optimized by extracting regions of interest and crown centroids. A multicenter dataset including 1,351 maxillary and 1,253 mandibular intraoral photographs was divided in an 8:2 ratio for model training and internal testing, and an additional 100 photographs were collected for external testing. The ground truth was obtained through measurements by experienced orthodontists using intraor

SUBMITTER: Wang R 

PROVIDER: S-EPMC12849715 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature