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Applications of Artificial Intelligence in Biomedical Diagnosis

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1287

Special Issue Editors


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Guest Editor
Department of Dermatology and Allergy, University Hospital, LMU Munich, 80336 Munich, Germany
Interests: neuroinflammation; molecular imaging; digital imaging; applied artificial intelligence; optical coherence tomography; confocal fluorescence microscopy

E-Mail Website
Guest Editor
Department of Dermatology and Allergy, University Hospital, LMU Munich, 80336 Munich, Germany
Interests: dermatology; dermatosurgery; dermato-oncology; melanoma and non-melanoma skin cancer; non-invasive diagnostics in dermatology; dermatopathology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue in the journal Applied Science that delves into the exciting realm of "Applications of Artificial Intelligence in Biomedical Diagnosis." As guest editors, we are delighted to invite researchers, scholars, and experts from the field of dermatology and beyond to contribute their valuable insights and research to this dynamic compilation.

In recent years, artificial intelligence (AI) has emerged as a transformative force, revolutionizing numerous aspects of healthcare and diagnostics. Within the realm of dermatology, up until now, the integration of AI-driven technologies in non-invasive imaging devices has shown promising results.

This Special Issue aims to explore the cutting-edge applications of AI in the context of non-invasive imaging in dermatology. We encourage submissions of original research, comprehensive reviews, and case studies that highlight the innovative utilization of AI algorithms, machine learning techniques, and computational tools in the diagnosis of various dermatological conditions.

We believe that this Special Issue will serve as a platform to foster collaborations, share expertise, and accelerate the translation of AI-based solutions into clinical practice.

We look forward to receiving your innovative research and making this Special Issue a resounding success.

Sincerely,

Dr. Maximilian Deußing
Prof. Dr. Daniela Hartmann
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

  • artificial intelligence
  • dermatology
  • non-invasive imaging
  • machine learning
  • virtual biopsy
  • teledermatology
  • histopathological images
  • image analysis algorithms

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Published Papers (1 paper)

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Research

17 pages, 2650 KiB  
Article
Machine Learning for Prediction of Cognitive Deterioration in Patients with Early Parkinson’s Disease
by Maitane Martinez-Eguiluz, Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Juan Carlos Gomez-Esteban, Iñigo Gabilondo and Ane Murueta-Goyena
Appl. Sci. 2024, 14(18), 8149; https://doi.org/10.3390/app14188149 - 11 Sep 2024
Viewed by 766
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder marked by motor and cognitive impairments. The early prediction of cognitive deterioration in PD is crucial. This work aims to predict the change in the Montreal Cognitive Assessment (MoCA) at years 4 and 5 from baseline [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative disorder marked by motor and cognitive impairments. The early prediction of cognitive deterioration in PD is crucial. This work aims to predict the change in the Montreal Cognitive Assessment (MoCA) at years 4 and 5 from baseline in the Parkinson’s Progression Markers Initiative database. The predictors included demographic and clinical variables: motor and non-motor symptoms from the baseline visit and change scores from baseline to the first-year follow-up. Various regression models were compared, and SHAP (SHapley Additive exPlanations) values were used to assess domain importance, while model coefficients evaluated variable importance. The LASSOLARS algorithm outperforms other models, achieving lowest the MAE, 1.55±0.23 and 1.56±0.19, for the fourth- and fifth-year predictions, respectively. Moreover, when trained to predict the average MoCA score change across both time points, its performance improved, reducing its MAE by 19%. Baseline MoCA scores and MoCA deterioration over the first-year were the most influential predictors of PD (highest model coefficients). However, the cumulative effect of other cognitive variables also contributed significantly. This study demonstrates that mid-term cognitive deterioration in PD can be accurately predicted from patients’ baseline cognitive performance and short-term cognitive deterioration, along with a few easily measurable clinical measurements. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Diagnosis)
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