Machine Learning and Deep Learning in Medical Imaging Diagnostics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 453

Special Issue Editor


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Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcalá de Henares, Ctra. Madrid-Barcelona, Km. 33,600, 28805 Alcalá de Henares, Spain
Interests: deep learning; neural networks; medical images
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Special Issue Information

Dear Colleagues,

The rapid growth of artificial intelligence has brought machine learning (ML) and deep learning (DL) to the forefront of medical imaging research and practice. These technologies are driving significant progress in diagnostic accuracy, efficiency, and reproducibility, offering unprecedented opportunities for clinicians and researchers alike. This Special Issue aims to explore the latest advances and applications of ML and DL in medical imaging diagnostics, including, but not limited to, image reconstruction, segmentation, classification, and quantitative analysis.

Particular attention will be given to challenges such as model interpretability, generalizability across populations and imaging modalities, and integration into real-world clinical workflows. Contributions addressing ethical considerations, data privacy, and the use of federated or privacy-preserving learning techniques are also encouraged. By showcasing a wide range of innovative studies and practical implementations, this Special Issue seeks to provide a comprehensive overview of how AI-driven approaches are transforming diagnostic imaging and shaping the future of precision medicine.

Dr. María Prados Privado
Guest Editor

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Keywords

  • medical imaging
  • machine learning
  • deep learning
  • diagnostic accuracy
  • artificial intelligence in healthcare

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Published Papers (1 paper)

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Research

16 pages, 1656 KB  
Article
Predicting Critical Failure Zones in Dental Implants: A Comparison of MLP and Random Forest Classifiers
by María Prados-Privado
Algorithms 2025, 18(12), 752; https://doi.org/10.3390/a18120752 - 28 Nov 2025
Viewed by 298
Abstract
Dental implants have excellent clinical results, but they still face a significant engineering hurdle: mechanical failure from repeated loading. Finite element simulations are widely used to identify areas of elevated stress in implant structures, but their computational cost makes them impractical for exhaustive [...] Read more.
Dental implants have excellent clinical results, but they still face a significant engineering hurdle: mechanical failure from repeated loading. Finite element simulations are widely used to identify areas of elevated stress in implant structures, but their computational cost makes them impractical for exhaustive scenario testing. This study proposes an artificial intelligence-based solution for rapidly predicting biomechanically critical conditions in dental implants. Specifically, two machine learning classifiers—a multilayer perceptron neural network and a Random Forest—were developed and compared. A dataset of 200 simulations was generated using finite element analysis by varying implant diameter, loading angle, and force magnitude. For each case, three biomechanical features were extracted: maximum von Mises stress, equivalent deformation, and fatigue safety factor. Risk cases were labeled based on a fatigue safety factor threshold. The neural network consisted of two hidden layers, while the Random Forest model comprised 100 decision trees. Both models were trained on 80% of the data and validated on the remaining 20%. The neural network achieved 99% classification accuracy, while the Random Forest reached 100%. The neural model demonstrated better sensitivity in identifying failure-prone scenarios, whereas the Random Forest provided better interpretability through feature importance analysis. These results highlight how artificial intelligence can be effectively integrated into the engineering workflow to support failure risk assessment in implant design and planning. The proposed surrogate models significantly reduce computation time and enable scalable, biomechanically informed decision-making. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Medical Imaging Diagnostics)
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