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Related Experiment Video

Updated: Jul 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Intra-oral scan segmentation using deep learning.

Shankeeth Vinayahalingam^1,2,3, Steven Kempers^1,2, Julian Schoep⁴

¹Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.

BMC Oral Health

|September 5, 2023

Summary

This summary is machine-generated.

This study introduces an automated deep learning system for precise teeth segmentation and labeling from intra-oral scans. The AI model achieves high accuracy, significantly improving efficiency in dental treatment planning.

Keywords:

Artificial intelligence Computer-assisted planning Deep learning Digital imaging Intra-oral scan

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Area of Science:

Dental technology
Artificial intelligence in medicine
Medical image analysis

Background:

Intra-oral scans (OS) are crucial for dental treatments, requiring accurate teeth segmentation.
Manual segmentation is time-consuming, subjective, and labor-intensive.
Automated methods are needed to improve efficiency and consistency.

Purpose of the Study:

To develop and validate a deep learning system for automated teeth segmentation and labeling from intra-oral scans.
To compare the performance of the automated system against manual segmentation.
To assess the potential of AI in streamlining dental workflows.

Main Methods:

A deep learning model combining PointCNN and 3D U-net was developed.
The model was trained and validated on 1400 intra-oral scans (OS).
Performance was evaluated using Intersection over Union (IoU) and FDI label accuracy on a test set of 350 OS.

Main Results:

The automated system achieved a mean IoU of 0.915 for teeth segmentation.
FDI tooth labeling accuracy reached a mean of 0.894.
Optical inspection confirmed excellent agreement with manual segmentation, with minor edge discrepancies.

Conclusions:

The proposed deep learning method offers a time-effective and observer-independent solution for teeth segmentation and labeling.
This technology shows promise for enhancing virtual treatment planning in various dental specialties.
Further clinical impact studies are recommended to explore its integration into practice.