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Applied Sciences
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Applications of Artificial Intelligence in Biomedical Diagnosis
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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 3706

Special Issue Editors

Dr. Maximilian Deußing

E-Mail Website
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
Prof. Dr. Daniela Hartmann

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 (3 papers)

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Research

20 pages, 15568 KiB  
Article
Line-Field Confocal Optical Coherence Tomography of Plaque Psoriasis Under IL-17 Inhibitor Therapy: Artificial Intelligence-Supported Analysis
by Hanna B. Wirsching, Oliver J. Mayer, Sophia Schlingmann, Janis R. Thamm, Stefan Schiele, Anna Rubeck, Wera Heinz, Julia Welzel and Sandra Schuh
Appl. Sci. 2025, 15(2), 535; https://doi.org/10.3390/app15020535 - 8 Jan 2025
Viewed by 701
Abstract
To date, therapeutic responses in plaque psoriasis are evaluated with clinical scores. No objective examination has been established. A recently developed non-invasive imaging tool, line-field confocal optical coherence tomography (LC-OCT), enables the in vivo live imaging of skin changes in psoriasis under therapy. [...] Read more.
To date, therapeutic responses in plaque psoriasis are evaluated with clinical scores. No objective examination has been established. A recently developed non-invasive imaging tool, line-field confocal optical coherence tomography (LC-OCT), enables the in vivo live imaging of skin changes in psoriasis under therapy. The aim of this study was to measure therapeutic response clinically and with LC-OCT, comparing the subjectively scored epidermal changes with an AI-supported analysis. This prospective, observational study included 12 patients with psoriasis starting a systemic treatment with IL-17 inhibitors (secukinumab, ixekizumab, and bimekizumab). LC-OCT and clinical assessment with a local psoriasis and severity index of the study plaque and a control area were performed before the initiation of therapy as well as after 4 and 12 weeks of treatment. A manual and AI-supported measurement of the thickness of epidermis, stratum corneum, and undulation of the dermo-epidermal junction was carried out. Acanthosis and hyperkeratosis showed a significant reduction under treatment. AI-supported calculations were compared to subjective measurements showing good reliability with high correlation. AI-supported analysis of vascular changes may serve as a prognostic and therapeutic response marker in the future. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Diagnosis)
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15 pages, 2874 KiB  
Article
3D Segmentation and Visualization of Skin Vasculature Using Line-Field Confocal Optical Coherence Tomography
by Oliver Mayer, Hanna Wirsching, Sophia Schlingmann, Julia Welzel and Sandra Schuh
Appl. Sci. 2025, 15(1), 159; https://doi.org/10.3390/app15010159 - 27 Dec 2024
Viewed by 984
Abstract
This study explores the advanced imaging of skin vasculature using Line-Field Confocal Optical Coherence Tomography (LC-OCT), which offers high-resolution, three-dimensional (3D) visualization of vascular structures, especially within skin tumors. The research aims to improve the understanding of tumor angiogenesis and the complex vascular [...] Read more.
This study explores the advanced imaging of skin vasculature using Line-Field Confocal Optical Coherence Tomography (LC-OCT), which offers high-resolution, three-dimensional (3D) visualization of vascular structures, especially within skin tumors. The research aims to improve the understanding of tumor angiogenesis and the complex vascular morphology associated with malignancies. The methodology involves converting original image stacks into negative images, manually tracing vessels using the Simple Neurite Tracer (SNT) plugin, and creating smoothed binary masks to reconstruct 3D models. The study’s results highlight the ability to visualize serpiginous, corkscrew-like, and irregular vessels across various skin cancers, including melanoma, squamous cell carcinoma, and basal cell carcinoma. These visualizations provide insights into vessel morphology, spatial arrangements, and blood flow patterns, which are crucial for assessing tumor growth and potential therapeutic responses. The findings indicate that 3D reconstructions from LC-OCT can uncover vascular details previously undetectable by two-dimensional imaging techniques, making it a valuable tool in dermatology for both clinical diagnostics and research. This method allows for better monitoring of skin cancer treatment and understanding of the role of vascular polymorphism in tumor development. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Diagnosis)
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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 1280
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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