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?
CBCT scans of C- shaped canals of patients aged 18 years or older, with satisfactory image quality, characterized by adequate sharpness, contrast and noise levels, enabling accurate delineation of pulp chambers and root canals. Additionally, the CBCT scans needed to have a field of view (FOV) covering the area of interest.
Patients younger than 18 years. CBCT scans with poor image quality (e.g., motion artifacts, excessive noise, low contrast, or beam hardening artifacts).
Retrospective collection of anonymized CBCT scans from the Faculty of Dentistry, Cairo University as well as private radiology service/ dental clinics and publicly available datasets.
Other
Retrospective
Not listed
Develop a deep learning framework for Automated Segmentation, classification of C- shaped canals.
An Attention U-Net based architecture will be explored, known for its ability to focus on important regions and efficiently process dental descriptors.
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