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Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.

Taseef H Farook¹, Farhan H Saad², Saif Ahmed²

¹Adelaide Dental School, University of Adelaide, Adelaide, AUS.

|December 14, 2023

Summary

This study introduces a deep learning tool for automatically identifying and segmenting teeth in dental images. The clinical annotation and segmentation tool (CAST) shows promise in streamlining dental image analysis.

Keywords:

artificial intelligence clinical photography deep learning digital dentistry radiology

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Accurate dental image analysis is crucial for diagnosis and treatment planning.
Manual annotation of dental images is time-consuming and prone to errors.

Purpose of the Study:

To develop and evaluate an unsupervised, deep learning-based tool for automated clinical annotation and segmentation (CAST).
To isolate clinically significant teeth in intraoral photographs and oral radiographs.

Main Methods:

Utilized a dataset of intraoral photographs and dental radiographs, augmented for training.
Employed YOLOv8 object detection and Segment Anything Model for auto-annotation and segmentation.
Incorporated X-AnyLabeling for manual reannotation and reinforcement learning.

Main Results:

Achieved a mean average precision (mAP) of 77.4% for tooth detection.
Demonstrated superior segmentation performance on intraoral images compared to radiographs.
The model successfully segmented tooth-related features and lesions without manual intervention.

Conclusions:

The developed CAST tool shows initial promise for automating dental image annotation and segmentation.
Further research is needed to overcome current limitations and enhance performance.
This technology has the potential to improve efficiency in dental diagnostics.