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Diagnostics
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Emerging Insights in Dental Diagnostics, Biomaterials and Digital Workflow
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Emerging Insights in Dental Diagnostics, Biomaterials and Digital Workflow

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2554

Editors

Prof. Dr. Marina Meleșcanu Imre

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Guest Editor
Department of Complete Denture, Faculty of Dental Medicine, “Carol Davila” University of Medicine and Pharmacy, 020221 Bucharest, Romania
Interests: biocompatible materials; poly(methyl methacrylate); interim dental prosthesis; three-dimensional printing; dental implants; digital dentistry
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Ana Maria Cristina Țâncu

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Guest Editor
Department of Prosthodontics, Faculty of Dentistry, “Carol Davila” University of Medicine and Pharmacy, 37 Dionisie Lupu Street, District 2, 020021 Bucharest, Romania
Interests: dental medicine; dentistry

Special Issue Information

Dear Colleagues,

The landscape of dental diagnostics, biomaterials, and digital workflows is undergoing a profound transformation, driven by rapid technological advancements and a growing emphasis on patient-centered care. Artificial intelligence (AI) has become a cornerstone of modern dental diagnostics, enabling clinicians to detect oral diseases such as caries, periodontal conditions, and even early malignancies with unprecedented accuracy and speed. AI-powered imaging and predictive analytics are not only enhancing diagnostic confidence but also streamlining treatment planning and reducing human error.

Simultaneously, the field of dental biomaterials is experiencing a renaissance, with innovations such as 3D printing, nanotechnology, and smart materials in prosthodontics.

New generations of biomaterials promise improved biocompatibility, durability, and even adaptive or self-healing properties, providing tailored solutions that work in tandem with the patient’s biology and support sustainable clinical practices.

The integration of these advances within digital workflows—from intraoral scanning and CAD/CAM systems to cloud-based lab solutions—has allowed digital dentistry to develop from an emerging trend to the new standard of care. Practices are now leveraging interconnected platforms that unite diagnostics, design, and manufacturing, resulting in more efficient, precise, and personalized dental care. This Special Issue will showcase leading research and perspectives on these pivotal developments, highlighting how emerging insights are shaping the future of dentistry.

Prof. Dr. Marina Meleșcanu Imre
Assoc. Prof. Ana Maria Cristina Țâncu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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 2600 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

  • dental diagnostics
  • artificial intelligence (AI)
  • digital dentistry
  • biomaterials
  • 3D printing
  • intraoral scanning
  • CAD/CAM
  • digital workflow
  • predictive analytics
  • clinical innovation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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16 pages, 2002 KB  
Article
Predictive In Vitro Diagnostic Screening of Strontium-Enriched Biodegradable Mg–Ca Alloys for Emerging Dental Applications
by Kamel Earar, Ciprian Adrian Dinu, Marius Valeriu Hînganu, Gabriela Leață, Corneliu Munteanu and Cristian Constantin Budacu
Diagnostics 2026, 16(7), 1060; https://doi.org/10.3390/diagnostics16071060 - 1 Apr 2026
Viewed by 1475
Abstract
Background: Biodegradable magnesium-based alloys are increasingly explored as emerging biomaterials for dental and maxillofacial applications due to their osteoconductive properties and potential to reduce long-term implant-related complications. However, early-stage evaluation requires predictive diagnostic screening methods capable of assessing cytocompatibility and cellular response [...] Read more.
Background: Biodegradable magnesium-based alloys are increasingly explored as emerging biomaterials for dental and maxillofacial applications due to their osteoconductive properties and potential to reduce long-term implant-related complications. However, early-stage evaluation requires predictive diagnostic screening methods capable of assessing cytocompatibility and cellular response under clinically relevant extract conditions. Objectives: In this study, Mg–0.5Ca alloys modified with increasing strontium concentrations (0.5–3 wt.%) were investigated through an in vitro diagnostic framework using MG-63 osteoblast-like cells. Methods: Cell viability was quantitatively assessed via MTT assays after 24 and 72 h of exposure, while fluorescence-based live-cell imaging provided complementary morphological insights. Results: demonstrated a composition-associated cytocompatibility profile, with Sr-enriched compositions showing improved cellular metabolic activity and adhesion patterns compared to lower-Sr compositions. Conclusions: These findings support the role of strontium as a functional alloying element and highlight the importance of standardized diagnostic screening workflows for emerging dental biomaterials. Overall, this study proposes a simplified predictive platform for early biocompatibility diagnostics, contributing to the integration of biomaterial evaluation into future digitalized dental regeneration workflows. Full article
(This article belongs to the Special Issue Emerging Insights in Dental Diagnostics, Biomaterials and Digital Workflow)
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Review

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27 pages, 1151 KB  
Review
Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models
by Laura Iosif, Marina Imre, Andreea Gabriela Wagner, Ana Maria Cristina Țâncu, Andreea Cristiana Didilescu, Hendrik Simon Brand, Andra-Ana-Maria Cîmpean, Radu Ilinca, Lucian Toma Ciocan and Vlad Gabriel Vasilescu
Diagnostics 2026, 16(12), 1801; https://doi.org/10.3390/diagnostics16121801 - 11 Jun 2026
Viewed by 521
Abstract
Orofacial pain (OFP) includes a broad spectrum of odontogenic and non-odontogenic conditions with overlapping clinical features that often limit diagnostic accuracy, driving increasing interest in artificial intelligence (AI) as a tool to enhance diagnostic precision and support clinical decision-making. A narrative review was [...] Read more.
Orofacial pain (OFP) includes a broad spectrum of odontogenic and non-odontogenic conditions with overlapping clinical features that often limit diagnostic accuracy, driving increasing interest in artificial intelligence (AI) as a tool to enhance diagnostic precision and support clinical decision-making. A narrative review was conducted using PubMed/MEDLINE, Scopus, and Web of Science to identify studies (2016–2026) applying AI to the diagnosis, classification, or prediction of OFP in adults. Eligible studies reported at least two diagnostic performance metrics and were thematically grouped into odontogenic and non-odontogenic categories, the latter including musculoskeletal, neurovascular, and neuropathic pain. Twenty studies were included. Neurovascular pain, particularly migraine, showed the most consistent and highest diagnostic performance, likely due to the greater availability of structured clinical data and standardized diagnostic criteria. Musculoskeletal pain, especially temporomandibular disorders, also demonstrated high and reproducible performance. In contrast, odontogenic pain showed lower and more heterogeneous performance, with better results mainly in imaging-based models, while signal- and behavior-based approaches were less robust. Neuropathic pain exhibited moderate to high performance in selected radiomics studies, but overall results remained inconsistent due to phenotypic variability and limited objective biomarkers. Currently, AI shows promising potential in OFP diagnosis, especially for neurovascular and musculoskeletal pain, but clinical translation is limited by data heterogeneity and lack of validation. Progress in clinical practice depends on multimodal datasets and multicenter studies to ensure robust, generalizable tools. Full article
(This article belongs to the Special Issue Emerging Insights in Dental Diagnostics, Biomaterials and Digital Workflow)
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