A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.

ABSTRACT:

Objectives

Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectively. The aim of this study was to use deep learning to automatically evaluate the diagnosis of CIRR in maxillary incisors using CBCT images.

Methods

A total of 50 cone beam computed tomography (CBCT) images and 176 incisors were selected for the present study. The maxillary incisors were manually segmented and labeled from the CBCT images by two independent radiologists as either healthy or affected by root resorption induced by the impacted canines. We used five different strategies for training the model: (A) classification using

SUBMITTER: Pirayesh Z 

PROVIDER: S-EPMC11344340 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature