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Diagnostics
Special Issues
Application of Neural Networks in Medical Diagnosis
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Application of Neural Networks in Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 625

Special Issue Editor

Dr. Hamid Reza Marateb

E-Mail Website
Guest Editor
BIOsignal Analysis for Rehabilitation and Therapy Research Group (BIOART), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain
Interests: biomedical signal processing; cognitive informatics in health and biomedicine; computer-assisted diagnosis and prognosis; medical data mining; neurological diagnostic techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neural networks have recently transformed diagnostic medicine by enhancing disease detection and prediction accuracy, efficiency, and adaptability across multiple healthcare domains. This Special Issue of Diagnostics is dedicated to presenting the latest breakthroughs in neural network applications for medical diagnostics, focusing on their strengths in interpreting complex datasets and supporting clinical decision-making.

Contributions are invited from researchers and professionals advancing neural network approaches—such as convolutional, recurrent, and deep architectures—to diagnose various conditions. Submissions that discuss algorithm innovations, clinical validation in real-world settings, and integration with conventional diagnostic methods are especially encouraged. The issue will emphasize studies that address the interpretability, reliability, and broad applicability of neural networks in clinical environments and those that consider the ethical and practical aspects of implementing these technologies. This compilation offers a thorough perspective on how neural networks reshape diagnostics, setting the stage for cutting-edge healthcare advancements.

Dr. Hamid Reza Marateb
Guest Editor

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. 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

  • neural networks in diagnostics
  • medical imaging analysis
  • deep learning in healthcare
  • predictive modeling for disease detection
  • clinical decision support systems
  • interpretability and explainability in AI
  • biomedical data processing
  • personalized medicine applications

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Published Papers (1 paper)

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Research

20 pages, 1638 KiB  
Article
Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence
by Wadi’ Othmani, Arthur Coste, Dimitri Papathanassiou and David Morland
Diagnostics 2025, 15(11), 1407; https://doi.org/10.3390/diagnostics15111407 - 31 May 2025
Viewed by 283
Abstract
Background/Objectives: 123I-FP-CIT dopamine transporter imaging is commonly used for the diagnosis of Parkinsonian syndromes in patients whose clinical presentation is atypical. Prolonged immobility, which can be difficult to maintain in this population, is required to perform SPECT acquisition. In this study we aimed [...] Read more.
Background/Objectives: 123I-FP-CIT dopamine transporter imaging is commonly used for the diagnosis of Parkinsonian syndromes in patients whose clinical presentation is atypical. Prolonged immobility, which can be difficult to maintain in this population, is required to perform SPECT acquisition. In this study we aimed to develop a Convolutional Neural Network (CNN) able to predict the outcome of the full examination based on the first acquired projection, and reliably detect normal patients. Methods: All 123I-FP-CIT SPECT performed in our center between June 2017 and February 2024 were included and split between a training and a validation set (70%/30%). An additional 100 SPECT were used as an independent test set. Examinations were labeled by two independent physicians. A VGG16-like CNN model was trained to assess the probability of examination abnormality from the first acquired projection (anterior and posterior view at 0°), taking age into consideration. A threshold maximizing sensitivity while maintaining good diagnostic accuracy was then determined. The model was validated in the independent testing set. Saliency maps were generated to visualize the most impactful areas in the classification. Results: A total of 982 123I-FP-CIT SPECT were retrieved and labelled (training set: 618; validation set: 264; independent testing set: 100). The trained model achieved a sensibility of 98.0% and a negative predictive value of 96.3% (one false negative) while maintaining an accuracy of 75.0%. The saliency maps confirmed that the regions with the greatest impact on the final classification corresponded to clinically relevant areas (basal ganglia and background noise). Conclusions: Our results suggest that this trained CNN could be used to exclude presynaptic dopaminergic loss with high reliability from the first acquired projection. It could be particularly useful in patients with compliance issues. Confirmation with images from other centers will be necessary. Full article
(This article belongs to the Special Issue Application of Neural Networks in Medical Diagnosis)
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