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Applied Sciences
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Artificial Intelligence (AI) Technologies in Biomedicine
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Artificial Intelligence (AI) Technologies in Biomedicine

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1667

Special Issue Editor

Dr. Anca Udristoiu

E-Mail Website
Guest Editor
Associate Professor, Faculty of Automation, Computers and Electronics, University of Craiova, 200440 Craiova, Romania
Interests: artificial intelligence; computer vision; software engineering; algorithm design; big data; machine learning; deep learning

Special Issue Information

Dear Colleagues,

We are seeking submissions for the publication of a Special Issue entitled Artificial Intelligence (AI) Technologies in Biomedicine.

Artificial intelligence (AI) has made significant contributions to the field of biomedicine, revolutionizing biomedical research. The integration of AI in biomedicine has great promise for enhancing diagnostics, drug discovery, personalized medicine, and overall patient care. Despite its numerous benefits, there are challenges and ethical considerations regarding the application of AI in biomedicine, namely ensuring data privacy, addressing ethical concerns, maintaining the transparency and interpretability of AI algorithms, and integrating AI technologies into existing healthcare systems.

This Special Issue will aim to explore the potential of using AI technologies in biomedicine to improve diagnostics, accelerate drug discovery, enable personalized medicine, and optimize healthcare operations. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Topics of interest for this Special Issue include but are not limited to:

  • Medical imaging analysis;
  • AI for diagnosis prognosis;
  • AI for genomics and personalized medicine;
  • AI for drug discovery and development;
  • Natural Language Processing (NLP) in Electronic Health Record (EHR) Analysis;
  • AI in medical robotics and surgery;
  • AI for remote patient monitoring.

Dr. Anca Udristoiu
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. 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

  • deep learning
  • machine learning
  • natural language processing
  • diagnosis prognosis
  • drug discovery
  • personalized medicine

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 2369 KiB  
Article
Machine Learning Models Based on [18F]FDG PET Radiomics for Bone Marrow Assessment in Non-Hodgkin Lymphoma
by Eva Milara, Pilar Sarandeses, Ana Jiménez-Ubieto, Adriana Saviatto, Alexander P. Seiffert, F. J. Gárate, D. Moreno-Blanco, M. Poza, Enrique J. Gómez, Adolfo Gómez-Grande and Patricia Sánchez-González
Appl. Sci. 2024, 14(22), 10291; https://doi.org/10.3390/app142210291 - 8 Nov 2024
Viewed by 540
Abstract
Non-Hodgkin lymphoma is a heterogeneous group of cancers that triggers bone marrow infiltration in 20–40% of cases. Bone marrow biopsy in combination with a visual assessment of [18F]FDG PET/CT images is used to assess the marrow status. Despite the potential of [...] Read more.
Non-Hodgkin lymphoma is a heterogeneous group of cancers that triggers bone marrow infiltration in 20–40% of cases. Bone marrow biopsy in combination with a visual assessment of [18F]FDG PET/CT images is used to assess the marrow status. Despite the potential of both techniques, they still have limitations due to the subjectivity of visual assessment. The present study aims to develop models based on bone marrow uptake in [18F]FDG PET/CT images at the time of diagnosis to differentiate bone marrow status. For this purpose, a model trained for skeleton segmentation and based on the U-Net architecture is retrained for bone marrow segmentation from CT images. The mask obtained from this segmentation together with the [18F]FDG PET image is used to extract radiomics features with which 11 machine learning models for marrow status differentiation are trained. The segmentation model yields very satisfactory results with Jaccard and Dice index values of 0.933 and 0.964, respectively. As for the classification models, a maximum F1_score_weighted and F1_score_macro of 0.962 and 0.747, respectively, are achieved. This highlights the potential of these features for bone marrow assessment, laying the foundation for a new clinical decision support system. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Technologies in Biomedicine)
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20 pages, 610 KiB  
Article
Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
by Hynek Bachratý, Peter Novotný, Monika Smiešková, Katarína Bachratá and Samuel Molčan
Appl. Sci. 2024, 14(20), 9315; https://doi.org/10.3390/app14209315 - 12 Oct 2024
Viewed by 559
Abstract
The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using [...] Read more.
The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov–Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Technologies in Biomedicine)
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