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Identification of dental implants using deep learning-pilot study.

Toshihito Takahashi¹, Kazunori Nozaki², Tomoya Gonda³

¹Department of Prosthodontics, Gerodontology and Oral Rehabilitation, Osaka University Graduate School of Dentistry, 1-8 Yamadaoka, Suita, Osaka, 565-0871, Japan. toshi-t@dent.osaka-u.ac.jp.

International Journal of Implant Dentistry

|September 22, 2020

Summary

This study introduces a deep learning system to identify unknown dental implant systems from panoramic X-rays. The AI model accurately detects implant systems, aiding dentists in resolving patient issues.

Keywords:

Artificial intelligence Deep learning Dental implant Object detection Yolov3

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Area of Science:

Artificial Intelligence in Dentistry
Medical Imaging Analysis
Deep Learning Applications

Background:

Dental implant identification can be challenging when the system is unknown to the clinician.
A reliable system is needed to identify implant systems from limited data, reducing reliance on dentist expertise.
This study addresses the need for an automated method to identify dental implant systems.

Purpose of the Study:

To develop and evaluate a deep learning model for identifying dental implant systems.
To create a system that can recognize various implant systems from radiographic images.

Main Methods:

Utilized a dataset of 1282 panoramic radiograph images containing dental implants.
Employed the Yolov3 object detection algorithm, implemented with TensorFlow and Keras.
Evaluated model performance using true positive (TP) ratio, average precision (AP), mean AP (mAP), and mean intersection over union (mIoU).

Main Results:

The model achieved a mean AP (mAP) of 0.71 and a mean IoU (mIoU) of 0.72.
The true positive ratio for individual implant systems ranged from 0.50 to 0.82.
Average precision for each implant system varied between 0.51 and 0.85.

Conclusions:

Deep learning-based object detection can effectively identify dental implants from panoramic radiographs.
The developed system shows potential to assist dentists and patients facing implant-related complications.
Further expansion of the image dataset is recommended to enhance model performance for clinical application.