A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.

Area of Science:

• Artificial Intelligence in Dentistry
• Medical Imaging Analysis
• Implantology

Background:

• Accurate identification of dental implant brands is crucial for successful treatment.
• Current methods for implant identification can be time-consuming and prone to error.
• Integrating advanced diagnostic tools can improve efficiency and patient outcomes in implantology.

Purpose of the Study:

• To develop an autonomous system for identifying dental implant brands using deep learning.
• To evaluate the system's performance and potential for clinical application.
• To propose a framework for enhancing diagnosis and treatment in implantology.

Main Methods:

• Utilized 28 deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers.
• Trained and evaluated models on a dataset of 1258 panoramic radiographs featuring six different implant systems.
• Focused on classification accuracy for various dental implant brands.

Main Results:

• The deep learning system demonstrated high classification accuracy for identifying dental implant brands.
• The ConvNeXt architecture's small model achieved a peak accuracy of 94.2%.
• This indicates significant success in automated implant brand classification.

Conclusions:

• Deep learning-based systems are highly effective for accurate dental implant type classification.
• The findings support the integration of deep learning into clinical practice for improved implantology.
• This technology promises to enhance patient care and treatment results.