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Related Experiment Video

Updated: Mar 15, 2026

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

Radiographic Data Segmentation as a Tool in Machine Learning and Deep Learning Artificial Intelligence Algorithms.

Ali Z Syed¹, Duygu Celik Ozen², Suayip Burak Duman³

¹Oral and Maxillofacial Medicine and Diagnostic Sciences, Case Western Reserve University School of Dental Medicine, 9601 Chester Avenue, Office #245C, Cleveland, OH 44106, USA.

Dental Clinics of North America

|March 13, 2026

Summary

This summary is machine-generated.

Machine learning and deep learning in dentistry excel at segmenting radiographic data for tasks like tooth numbering and lesion detection. These artificial intelligence methods enhance accuracy and efficiency, often matching or exceeding human performance.

Keywords:

Artificial intelligence Machine learning Radiographic data segmentation

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Area of Science:

Dentistry
Radiology
Artificial Intelligence

Background:

Radiographic data segmentation is crucial for machine learning (ML) and deep learning (DL) applications in dentistry.
Understanding artificial intelligence (AI), ML, and DL concepts is foundational for this field.

Purpose of the Study:

To review the role of radiographic data segmentation in AI-driven dental diagnostics.
To highlight the capabilities of convolutional neural networks (CNNs) in various dental imaging modalities.

Main Methods:

Review of AI, ML, and DL concepts.
Focus on CNN-driven tasks including classification, detection, and pixel/voxel segmentation.
Application across panoramic, periapical, bitewing, and cone beam computed tomography (CBCT) imaging.

Main Results:

Automated tasks like tooth numbering, restoration/implant labeling, caries delineation, and lesion detection show high performance.
AI methods frequently match or surpass clinician accuracy.
Significant acceleration of dental workflow processes observed.

Conclusions:

AI demonstrates substantial potential to improve diagnostic accuracy and efficiency in dentistry.
Essential human oversight remains a critical component in AI-assisted dental workflows.
ML and DL are transforming dental radiographic analysis.