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Related Experiment Video

Updated: Jun 13, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Technical error identification in intraoral radiographs using multiple convolutional neural networks: an exploratory

Mariam Baghdady1,2, Jagan Kumar Baskaradoss3, Sabarinath Prasad4

  • 1Oral and Maxillofacial Radiology, Department of Diagnostic Sciences, College of Dentistry, Kuwait University, P.O. Box: 24923, 13110, Safat, Kuwait. mariam.baghdady@ku.edu.kw.

Oral Radiology
|June 11, 2026
PubMed
Summary

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Artificial intelligence (AI) shows promise for automatically detecting errors in intraoral radiographs. Convolutional neural networks (CNNs) can identify common issues like cone cuts and overlaps, improving diagnostic reliability.

Failed At:

2026-06-19T13:40:45.993031+00:00

Keywords:
Artificial intelligenceAutomated algorithmCNN architecturesDeep learningGoogleNetResNet 50radiographic errors

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Last Updated: Jun 13, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024