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Prof. Dr. André Luiz Ferreira Costa

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

Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes

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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
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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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12 pages, 2242 KB

Open AccessArticle

Augmented Reality-Assisted Micro-Invasive Apicectomy with Markerless Visual–Inertial Odometry: An In Vivo Pilot Study

by Marco Farronato, Davide Farronato, Federico Michelini and Giulio Rasperini

Appl. Sci. 2025, 15(23), 12588; https://doi.org/10.3390/app152312588 - 27 Nov 2025

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Abstract

Introduction: Apicectomy is an endodontic surgical procedure prescribed for persistent periapical pathologies when conventional root canal therapy or retreatment have failed. Accurate intraoperative visualization of the root apex and surrounding structures remains challenging and subject to possible errors. Augmented reality (AR) allows for the addition of real-time digital overlays of the anatomical region, thus potentially improving surgical precision and reducing invasiveness. The purpose of this pilot study is to describe the application of an AR method in cases requiring apicectomy. Materials and Methods: Patients presenting with chronic persistent apical radio-translucency associated with pain underwent AR-assisted apicectomy. Cone-beam computed tomography (CBCT) scans were obtained preoperatively for segmentation of the target root apex and adjacent anatomical structures. A custom visual–inertial odometry (VIO) algorithm was used to map and stabilize the segmented digital 3D models on a portable device in real time, enabling an overlay of digital guides onto the operative field. The duration of preoperative procedures, was recorded. Postoperative pain measured by a Visual Analogue Scale (VAS), and periapical healing assessed with radiographic evaluations, were recorded at baseline (T0) and at 6 weeks and 6 months (T1–T2) after surgery. Results: AR-assisted apicectomies were successfully performed in all three patients without intraoperative complications. The digital overlap procedure required an average of [1.49 ± 0.34] minutes. VAS scores decreased significantly from T0 to T2, and patients showed radiographic evidence of progressive periapical healing. No patient reported persistent discomfort at follow-up. Conclusion: This preliminary pilot study indicates that AR-assisted apicectomy is feasible and may improve intraoperative visualization with low additional surgical time. Future larger-scale studies with control groups are needed to validate the method proposed and to quantify the outcomes. Clinical Significance: By integrating real-time digital images of bony structures and root morphology, AR guidance during apicectomy may offer enhanced precision for apical resection and may decrease the risk of iatrogenic damage. The use of a visual–inertial odometry-based AR method is a novel technique that demonstrated promising results in terms of VAS and final outcomes, especially in anatomically challenging cases in this preliminary pilot study. Full article

(This article belongs to the Special Issue Advanced Dental Imaging Technology)

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16 pages, 2173 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2453

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: 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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