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Advanced Dental Imaging Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Dentistry and Oral Sciences".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 2006

Special Issue Editors


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Guest Editor
Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 1506-000, SP, Brazil
Interests: dentomaxillofacial radiology; MRI; CBCT; computer-assisted diagnosis; TMJ

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Guest Editor
Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos 2245-000, SP, Brazil
Interests: dentomaxillofacial radiology; MRI; CBCT; computer-assisted diagnosis; TMJ
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This issue focuses on the comprehensive exploration of advanced dental imaging technology, encompassing its development, implementation, and impact on modern dentistry. Advanced dental imaging technologies are based on cutting-edge systems that combine structural accuracy with functional diagnostics. One of the most significant advancements is cone-beam computed tomography (CBCT), which provides high-resolution 3D images of dental structures. Additionally, digital radiography has revolutionized traditional X-ray techniques, offering enhanced image quality and reduced radiation exposure. The integration of artificial intelligence in dental imaging has broadened the possibilities for automated diagnosis, treatment planning, and image analysis. These technologies are being rapidly adopted across various dental specialties, addressing critical needs in areas such as orthodontics, endodontics, and oral surgery. The continuous evolution of these imaging modalities promises to improve diagnostic accuracy, treatment outcomes, and patient care, ultimately contributing to the advancement of dental health and education.

Prof. Dr. André Luiz Ferreira Costa
Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes
Guest Editors

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Keywords

  • cone-beam computed tomography (CBCT)
  • digital radiography
  • artificial intelligence
  • radiomics, three-dimensional

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Published Papers (1 paper)

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Research

16 pages, 2173 KB  
Article
Application of AI-Driven Software Diagnocat in Managing Diagnostic Imaging in Dentistry: A Retrospective Study
by Haris Mema, Elona Gaxhja, Ylli Alicka, Mitilda Gugu, Skender Topi, Mario Giannoni, Davide Pietropaoli and Serena Altamura
Appl. Sci. 2025, 15(17), 9790; https://doi.org/10.3390/app15179790 - 6 Sep 2025
Viewed by 1477
Abstract
Background: This study investigates the diagnostic reliability of an artificial intelligence (AI)-based software (Diagnocat) in caries, dental restorations, missing teeth, and periodontal bone loss on panoramic radiographs (PRs), comparing its performance with evaluations from three independent dental experts serving as ground truth. Methods: [...] Read more.
Background: This study investigates the diagnostic reliability of an artificial intelligence (AI)-based software (Diagnocat) in caries, dental restorations, missing teeth, and periodontal bone loss on panoramic radiographs (PRs), comparing its performance with evaluations from three independent dental experts serving as ground truth. Methods: A total of 104 PRs were analyzed using Diagnocat, which assigned a likelihood score (0–100%) for each condition. The same images were independently evaluated by three experts. The diagnostic performance of Diagnocat was evaluated using sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis, while inter-rater agreement was assessed through Cohen’s kappa (κ). Results: Diagnocat showed high overall sensitivity (99.2%), identifying nearly all conditions marked as present by human evaluators. Specificity was low (8.7%), indicating a tendency to overdiagnose. Overall accuracy was 96%, likely influenced by the coexistence of multiple conditions. Sensitivity ranged from 77% to 96%, while specificity varied: dental restorations (66%), missing teeth (68%), periodontal bone loss (71%), and caries signs (47%). The agreement was fair for dental restorations (κ = 0.39) and missing teeth (κ = 0.37), but poor for caries signs (κ = −0.15) and periodontal bone loss (κ = −0.62). Conclusions: Diagnocat shows promise as a screening tool due to its high sensitivity, but low specificity and poor agreement for certain conditions warrant cautious interpretation alongside clinical evaluation. Full article
(This article belongs to the Special Issue Advanced Dental Imaging Technology)
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