Performance evaluation of deep learning models for the classification and identification of dental implants.

Statement Of Problem: Dental implant systems can be identified using image classification deep learning. However, investigations on the accuracy of classifying and identifying implant design through an object detection model are lacking.

Purpose: The purpose of this study was to evaluate the performance of an object detection deep learning model for classifying the implant designs of 103 types of implants.

Identification of 130 Dental Implant Types Using Ensemble Deep Learning.

To evaluate the accuracy and clinical usability of an identification model using ensemble deep learning for 130 dental implant types. A total of 28,112 panoramic radiographs were obtained from 30 domestic and foreign dental clinics. From these panoramic radiographs, 45,909 implant fixture images were extracted and labeled based on electronic medical records.

Monolithic Zirconia FPD on Modified Titanium Bonding Bases in Limited Interocclusal Distance: A Case Report.

A 62-year-old male patient sought treatment for missing maxillary teeth. A diagnostic cast demonstrated that the interocclusal distance was insufficient. A 5-unit screw-retained implant-supported fixed partial denture (FPD) was used to restore missing maxillary teeth.