Automated Identification of Dental Implants Using Artificial Intelligence

Area of Science:

• Dental Radiology
• Artificial Intelligence in Medicine
• Biomedical Imaging Analysis

Background:

• Accurate identification of dental implant brands is crucial for treatment planning and follow-up.
• Current methods for implant identification can be time-consuming and may lack precision.

Purpose of the Study:

• To develop and assess an AI-powered computer-assisted system for identifying dental implant manufacturers.
• To evaluate the accuracy of this system using digital periapical radiographs.

Main Methods:

• A dataset of 1,800 digital periapical radiographs from three manufacturers was utilized.
• A convolutional neural network (CNN) model was developed and trained on 80% of the data.
• The CNN's performance was evaluated using accuracy, sensitivity, specificity, and ROC curve analysis on the remaining 20% of the data.

Main Results:

• The CNN system achieved high accuracy rates: 99.78% on training data and 99.36% on testing data.
• Validation data showed an accuracy of 85.29%, reflecting the system's learning process.
• The algorithm demonstrated strong diagnostic performance in identifying implant manufacturers.

Conclusions:

• Convolutional neural networks (CNNs) are effective for accurately identifying dental implant manufacturers from radiographs.
• This AI-driven approach offers a precise and clinically significant method for implant identification.
• The developed system holds potential for improving efficiency and accuracy in dental practice.