Clinical Research Directory

Deep Learning Framework for Classification, 3D Segmentation & Visualization of C-shaped Canals

Sponsor: Cairo University

Summary

The goal of this retrospective diagnostic accuracy study is to develop and validate a deep learning framework for the automated classification, three-dimensional (3D) segmentation, and visualization of C-shaped root canal anatomy using cone-beam computed tomography (CBCT) scans in adults with C-shaped root canals. The main questions it aims to answer are: Can a deep learning model accurately classify C-shaped root canal configurations from CBCT images? Can the model precisely segment the complex 3D anatomy of C-shaped root canals, including fins, webs, and isthmuses, with accuracy comparable to expert endodontists? Can the automated framework improve the efficiency and clinical utility of diagnosing and visualizing C-shaped root canal anatomy?

Official title: Diagnostic Accuracy of a Deep Learning Framework for Automated Classification, 3D Segmentation and Comprehensive Visualization of C-shaped Root Canal Architecture From Cone-Beam Computed Tomography

Key Details

Conditions