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Tooth Anatomy

Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or grinding food.

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Deep Learning-Based Algorithm for Staging Secondary Caries in Bitewings.

Niels van Nistelrooij^1,2, Eduardo Trota Chaves^3,4, Maximiliano Sergio Cenci³

¹Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.

Caries Research

|October 29, 2024

Summary

This summary is machine-generated.

This study developed an artificial intelligence algorithm for detecting secondary caries around dental restorations. The convolutional neural network (CNN) shows high accuracy in identifying these lesions, aiding clinical decisions.

Keywords:

Artificial intelligence Caries detection Convolutional neural network Diagnostic imaging Radiography

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Area of Science:

Artificial Intelligence in Dentistry
Dental Radiology
Machine Learning for Medical Imaging

Background:

Limited AI tools exist for secondary caries detection and staging.
Secondary caries is a significant clinical challenge around dental restorations.

Purpose of the Study:

Develop a convolutional neural network (CNN)-based algorithm for secondary caries detection and staging.
Utilize a novel approach for determining lesion severity.

Main Methods:

Trained a Mask R-CNN with Swin Transformer on 2,612 restored teeth from bitewing radiographs.
Employed two-stage training for enhanced detection and severity assessment.
Validated algorithm performance using expert annotations and statistical metrics.

Main Results:

Achieved high specificity for detecting all lesions (0.966 ± 0.025) and dentine lesions (0.964 ± 0.019).
Demonstrated strong correlation (0.802) between algorithm and expert severity scores.
Area under ROC curves were 0.940 for all lesions and 0.946 for dentine lesions.

Conclusions:

Developed an improved AI algorithm to aid clinicians in detecting and staging secondary caries.
The algorithm incorporates an innovative annotation approach, treating lesion severity as a continuous outcome.
This tool supports better clinical decision-making in restorative dentistry.