Theological and Societal Dimensions of Takfir: an Analysis Within the Context of Propaganda and Hate Speech
Year: 2025
Type: article
Abstract: Root fracture detection in CBCT images is vital for precise dental treatment planning. This study aims to evaluate the performance of an artificial intelligence (AI)-driven decision-making system utilizing a VGG19-based convolutional neural network (CNN) for automated root fracture identification. A dataset comprising 50 CBCT images was used, split into 25 fractured and 25 non-fractured cases. The model achieved an overall accuracy of 92%, with sensitivity and specificity rates of 90% and 93%, respectively. These results underscore the potential of AI in enhancing diagnostic accuracy, efficiency, and reliability in dental radiology, paving the way for its integration into clinical workflows.
Author: Dr. Mahmut Mert ASLAN
Institution: Selçuk University
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409072959