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Deep Learning Applied in Dentistry: Challenges and Prospects
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Deep Learning Applied in Dentistry: Challenges and Prospects

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 February 2026 | Viewed by 681

Special Issue Editor

Dr. María Prados-Privado

E-Mail Website
Guest Editor
Higher Polytechnic School, Universidad Internacional de La Rioja, Av. de la Paz, 137, 26006 Logroño, Spain
Interests: neural networks; artificial intelligence; dentistry; medical images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of engineering, artificial intelligence (AI), and clinical dental practice is revolutionizing the field of modern dentistry. Among the most transformative developments is the application of deep learning techniques, which are enhancing diagnostic accuracy, treatment planning, and patient-specific interventions. This Special Issue aims to explore the intersection of AI-driven technologies with dental science, particularly focusing on image analysis, predictive modeling, and guided surgery systems.

Deep learning models, such as convolutional neural networks (CNNs) and transformer architectures, are now being used to detect pathologies from radiographs, segment anatomical structures in 3D scans, and optimize surgical workflows through real-time decision support. These advancements are made possible through interdisciplinary collaboration between dental clinicians, biomedical engineers, and computer scientists. Despite notable progress, significant challenges remain in terms of data standardization, model interpretability, and clinical validation. This Special Issue welcomes original research, reviews, and case studies that push the boundaries of how AI can safely and effectively be integrated into dental and surgical practice.

Dr. María Prados-Privado
Guest Editor

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Keywords

  • deep learning
  • artificial intelligence in dentistry
  • computer-aided surgery
  • medical image analysis
  • dental radiology
  • guided dental surgery
  • biomedical engineering
  • convolutional neural networks
  • predictive modeling in oral health
  • clinical decision support systems

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Published Papers (2 papers)

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Research

15 pages, 981 KB  
Article
Integrating Finite Element Data with Neural Networks for Fatigue Prediction in Titanium Dental Implants: A Proof-of-Concept Study
by Tomás Gandía-Sastre and María Prados-Privado
Appl. Sci. 2025, 15(19), 10362; https://doi.org/10.3390/app151910362 - 24 Sep 2025
Viewed by 58
Abstract
Background: Titanium dental implants are widely used, but their long-term mechanical reliability under fatigue loading remains a key concern. Traditional finite element analysis is accurate but computationally intensive. This study explores the integration of finite element analysis data with neural networks to predict [...] Read more.
Background: Titanium dental implants are widely used, but their long-term mechanical reliability under fatigue loading remains a key concern. Traditional finite element analysis is accurate but computationally intensive. This study explores the integration of finite element analysis data with neural networks to predict fatigue-related responses efficiently. Methods: A dataset of 200 finite element analysis simulations was generated, varying load intensity, load angle, and implant size. Each simulation provided three outputs: maximum von Mises stress, maximum displacement, and fatigue safety factor. A feedforward neural network with two hidden layers (64 neurons each, ReLU activation) was trained using 160 simulations, with 40 reserved for testing. Results: The neural network achieved high accuracy across all outputs, with R2 values of 0.97 for stress, 0.95 for deformation, and 0.92 for the fatigue safety factor. Mean errors across the test set were below 5%, indicating strong predictive performance under diverse conditions. Conclusions: The findings demonstrate that neural networks can reliably replicate finite element analysis outcomes with significantly reduced computational time. This approach offers a promising tool for accelerating implant assessment and supports the growing role of AI in biomechanical design and analysis. Full article
(This article belongs to the Special Issue Deep Learning Applied in Dentistry: Challenges and Prospects)
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21 pages, 1602 KB  
Article
A Forensic Odontology Application: Impact of Image Quality on CNNs for Dental Status Analysis from Orthopantomograms
by Ajla Zymber Çeshko, Ivana Savić Pavičin, Denis Milošević, Luka Banjšak, Marko Subašić and Marin Vodanović
Appl. Sci. 2025, 15(18), 10265; https://doi.org/10.3390/app151810265 - 21 Sep 2025
Viewed by 218
Abstract
Artificial Intelligence, especially Convolutional Neural Networks (CNN), is gaining importance in health sciences, including forensic odontology. This study aimed to systematically analyze elements for automated dental status registration on OPGs using CNNs, on different image segments and resolutions. A dataset of 1400 manually [...] Read more.
Artificial Intelligence, especially Convolutional Neural Networks (CNN), is gaining importance in health sciences, including forensic odontology. This study aimed to systematically analyze elements for automated dental status registration on OPGs using CNNs, on different image segments and resolutions. A dataset of 1400 manually annotated digital OPGs was divided into train, test, and validation sets (75%–12.5%–12.5%). Pre-trained and from-scratch models were developed and evaluated on images from full OPGs to individual and segmented teeth and sizes from 256 px to 1820 px. Performance was measured by Sørensen–Dice coefficient for segmentation and mean average precision (mAP) for detection. For segmentation, the UNet Big model was the most successful, using segmented or individual images, achieving 89.14% for crown and 84.90% for fillings, and UNet with 79.09% for root canal fillings. Caries presented a significant challenge, with the UNet model achieving the highest score of 64.68%. In detection, YOLOv5x6, trained from scratch, achieved the highest mAP of 98.02% on 1820 px images. Larger image resolutions and individual tooth inputs significantly improved performance. This study confirms the success of CNN models in specific tasks on OPGs. Image quality and input (individual tooth, resolutions above 640 px) critically influenced model competence. Further research with from-scratch models, higher resolutions, and smaller image segments is recommended. Full article
(This article belongs to the Special Issue Deep Learning Applied in Dentistry: Challenges and Prospects)
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