The journal of evidence-based dental practice
39947781
INTRODUCTION AND OBJECTIVE: Dental implants are well-established for restoring partial or complete tooth loss, with osseointegration being essential for their long-term success. Peri-implantitis, marked by inflammation and bone loss, compromises impl...
Oral implant surgery demands a high level of precision and expertise, making the integration of robotic assistance an optimal solution. This study introduces an innovative dental implant robotic system designed to enhance accuracy during cavity prepa...
In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group ...
This study aims to explore the effect of implant apex design, osteotomy preparation, intraosseous depth and bone quality on immediate implant placement insertion torque and establish a more sophisticated decision model with multi-factor analysis base...
Dental implant surgery is now a well versed approach for tooth replacement, addressing various limitations of fixed bridges and removable dentures, thereby reinstating both the form and function of missing teeth. However, it is technically sensitive ...
The aim of this study was to evaluate the reliability and quality of information generated by ChatGPT regarding dental implants and peri-implant phenotypes. A structured questionnaire on these topics was presented to the AI-based chatbot, and its res...
With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate i...
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.
OBJECTIVE: This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.
OBJECTIVES: This clinical study aimed to compare the accuracy of implant placement obtained using a robotic system and a full-guide template in patients with dentition defects.