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Applied Sciences
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Oral Diseases: Diagnosis and Therapy
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Oral Diseases: Diagnosis and Therapy

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 2011

Special Issue Editors

Prof. Dr. Alberta Lucchese

E-Mail Website
Guest Editor
Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania Luigi Vanvitelli, Via Luigi de Crecchio, 6, 80138 Naples, Italy
Interests: dentistry; oral medicine; oral pathology; oral immunology; imaging in oral diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Dario Di Stasio

E-Mail Website
Guest Editor
Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania "Luigi Vanvitelli", 80138 Napoli, Italy
Interests: dentistry; oral medicine; oral pathology; oral immunology; imaging in oral diseases, intraoral ultrasonography, image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue entitled "Oral Diseases: Diagnosis and Therapy".

The diagnostic process is often long and challenging to coordinate, and must include multidisciplinary collaboration between dentists, general practitioners, pathologists, and surgeons.

Several non-invasive imaging techniques have recently been applied to the oral cavity. These techniques can provide additional information during clinical examination to shorten the duration of biopsy and guide it by selectively identifying and targeting only highly suspicious lesions at their most representative sites.

Moreover, clinical efforts and scientific research are increasingly pursuing the development of new assistive tools and minimally invasive techniques for novel therapeutic approaches. Photodynamic concepts have emerged as a considerable area of development for diagnostic and therapeutic clinical applications.

This Special Issue aims to collect high-quality original research articles, reviews, case series, and case reports on diagnosing and treating oral diseases.

We look forward to receiving your contributions.

Prof. Dr. Alberta Lucchese
Dr. Dario Di Stasio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dentistry
  • oral surgery
  • diagnostic devices
  • imaging in oral medicine
  • oral diagnosis
  • photodynamic therapy
  • oral autoimmune diseases
  • oral potentially malignant disorders (OPMD)
  • oral carcinoma
  • oral manifestations in patients with systemic diseases

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Published Papers (2 papers)

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Research

13 pages, 1075 KiB  
Article
Elevated BP180 ELISA at Diagnosis Correlates with Disease Severity and Relapse in Oral Mucous Membrane Pemphigoid: Preliminary Results from a Retrospective Monocentric Italian Study
by Andrea Gabusi, Davide B. Gissi, Roberto Rossi, Federica Filippi, Camilla Loi, Cosimo Misciali, Giacomo Clarizio, Michelangelo La Placa and Federico Bardazzi
Appl. Sci. 2025, 15(2), 689; https://doi.org/10.3390/app15020689 - 12 Jan 2025
Viewed by 363
Abstract
Background: Little is known about the relevance of BP180 ELISA for the clinical management of oral mucous membrane pemphigoid (OMMP). The aim of the present study was to investigate if the levels of anti-BP180 antibodies at diagnosis could be correlated with clinical severity [...] Read more.
Background: Little is known about the relevance of BP180 ELISA for the clinical management of oral mucous membrane pemphigoid (OMMP). The aim of the present study was to investigate if the levels of anti-BP180 antibodies at diagnosis could be correlated with clinical severity and relapse. Methods: The present study included 44 OMMP patients with positive direct immunofluorescence (DIF). Circulating anti-BP180 IgG was measured using the same available ELISA kit (Euroimmun cut-off 20 U/mL). Clinical severity at diagnosis was measured using the oral disease severity score (ODSS). Only patients who achieved clinical remission (CR) were included in the analysis of variables related to relapse. Relapse was calculated as the interval between the date of the best type of clinical remission achieved and the date of relapse. Results: Values of BP180 > 20 U/mL significantly correlated with higher ODSSs in both univariate (p < 0.05) and multivariate analyses (p < 0.05). Among 39/44 patients who achieved CR, 17/39 relapsed. Kaplan–Meier analysis revealed that patients with BP180 > 20 U/mL displayed worse clinical behavior in terms of relapse (p < 0.05). Conclusion: BP180 ELISA at diagnosis appears to be a useful parameter to stratify OMMP patients with more severe disease and worse clinical outcomes after clinical remission. Full article
(This article belongs to the Special Issue Oral Diseases: Diagnosis and Therapy)
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10 pages, 1304 KiB  
Article
Age and Sex Estimation in Children and Young Adults Using Panoramic Radiographs with Convolutional Neural Networks
by Tuğçe Nur Şahin and Türkay Kölüş
Appl. Sci. 2024, 14(16), 7014; https://doi.org/10.3390/app14167014 - 9 Aug 2024
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Abstract
Image processing with artificial intelligence has shown significant promise in various medical imaging applications. The present study aims to evaluate the performance of 16 different convolutional neural networks (CNNs) in predicting age and gender from panoramic radiographs in children and young adults. The [...] Read more.
Image processing with artificial intelligence has shown significant promise in various medical imaging applications. The present study aims to evaluate the performance of 16 different convolutional neural networks (CNNs) in predicting age and gender from panoramic radiographs in children and young adults. The networks tested included DarkNet-19, DarkNet-53, Inception-ResNet-v2, VGG-19, DenseNet-201, ResNet-50, GoogLeNet, VGG-16, SqueezeNet, ResNet-101, ResNet-18, ShuffleNet, MobileNet-v2, NasNet-Mobile, AlexNet, and Xception. These networks were trained on a dataset of 7336 radiographs from individuals aged between 5 and 21. Age and gender estimation accuracy and mean absolute age prediction errors were evaluated on 340 radiographs. Statistical analyses were conducted using Shapiro–Wilk, one-way ANOVA, and Tukey tests (p < 0.05). The gender prediction accuracy and the mean absolute age prediction error were, respectively, 87.94% and 0.582 for DarkNet-53, 86.18% and 0.427 for DarkNet-19, 84.71% and 0.703 for GoogLeNet, 81.76% and 0.756 for DenseNet-201, 81.76% and 1.115 for ResNet-18, 80.88% and 0.650 for VGG-19, 79.41% and 0.988 for SqueezeNet, 79.12% and 0.682 for Inception-Resnet-v2, 78.24% and 0.747 for ResNet-50, 77.35% and 1.047 for VGG-16, 76.47% and 1.109 for Xception, 75.88% and 0.977 for ResNet-101, 73.24% and 0.894 for ShuffleNet, 72.35% and 1.206 for AlexNet, 71.18% and 1.094 for NasNet-Mobile, and 62.94% and 1.327 for MobileNet-v2. No statistical difference in age prediction performance was found between DarkNet-19 and DarkNet-53, which demonstrated the most successful age estimation results. Despite these promising results, all tested CNNs performed below 90% accuracy and were not deemed suitable for clinical use. Future studies should continue with more-advanced networks and larger datasets. Full article
(This article belongs to the Special Issue Oral Diseases: Diagnosis and Therapy)
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