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Methods, Applications and Developments in Biomedical Informatics: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2378

Special Issue Editors


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Guest Editor
Computer Science Institute DiSIT, University of Piemonte Orientale "Amedeo Avogadro", 15121 Alessandria, Italy
Interests: artificial intelligence; biomedical applications of AI; business process management; case based reasoning; temporal abstractions; computational ontologies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Cardiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
Interests: data standardization; medical technology; data exchange; e-health; biomedical informatics; privacy and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer science and artificial intelligence play a central role in the healthcare domain in various aspects, e.g., clinical research, decision support, process organization, management and optimization, telemedicine, and public health. In this Special Issue, we encourage the submission of original research articles, review articles, and short technical communications that focus on the above topics and areas. More specifically, we seek methodological articles on applications and recent developments in the context of artificial intelligence and biomedical informatics applied to the healthcare domain.

Topics include, but are not limited to, the following:

  • Artificial intelligence;
  • Data mining/machine learning;
  • Clinical guidelines;
  • Decision support and therapy improvement;
  • Business process management/process mining;
  • Health data acquisition and analysis;
  • Healthcare information systems/knowledge representation/reasoning;
  • Medical imaging and pattern recognition;
  • Architectures and technologies for telehealth;
  • Medical signal and data processing.

Dr. Giorgio Leonardi
Dr. Enno van der Velde
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
  • data mining/machine learning
  • clinical guidelines
  • decision support and therapy improvement
  • business process management/process mining
  • health data acquisition and analysis
  • healthcare information systems/knowledge representation/reasoning
  • medical imaging and pattern recognition
  • architectures and technologies for telehealth
  • medical signal and data processing

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Published Papers (2 papers)

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Research

21 pages, 5998 KiB  
Article
Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction Methods in Neonatal MRI
by Tânia F. Vaz, Nima Naseh, Lena Hellström-Westas, Nuno Canto Moreira, Nuno Matela and Hugo A. Ferreira
Appl. Sci. 2024, 14(24), 11575; https://doi.org/10.3390/app142411575 - 11 Dec 2024
Viewed by 947
Abstract
Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume [...] Read more.
Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA. Full article
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25 pages, 723 KiB  
Article
Noninvasive Deep Learning Analysis for Smith–Magenis Syndrome Classification
by Esther Núñez-Vidal, Raúl Fernández-Ruiz, Agustín Álvarez-Marquina, Irene Hidalgo-delaGuía, Elena Garayzábal-Heinze, Nikola Hristov-Kalamov, Francisco Domínguez-Mateos, Cristina Conde and Rafael Martínez-Olalla
Appl. Sci. 2024, 14(21), 9747; https://doi.org/10.3390/app14219747 - 25 Oct 2024
Viewed by 923
Abstract
Smith–Magenis syndrome (SMS) is a rare, underdiagnosed condition due to limited public awareness of genetic testing and a lengthy diagnostic process. Voice analysis can be a noninvasive tool for monitoring and detecting SMS. In this paper, the cepstral peak prominence and mel-frequency cepstral [...] Read more.
Smith–Magenis syndrome (SMS) is a rare, underdiagnosed condition due to limited public awareness of genetic testing and a lengthy diagnostic process. Voice analysis can be a noninvasive tool for monitoring and detecting SMS. In this paper, the cepstral peak prominence and mel-frequency cepstral coefficients are used as disease monitoring and detection metrics. In addition, an efficient neural network, incorporating synthetic data processes, was used to detect SMS in a cohort of individuals with the disease. Three study cases were conducted with a set of 19 SMS patients and 292 controls. The three study cases employed various oversampling and undersampling techniques, including SMOTE, random oversampling, NearMiss, random undersampling, and 16 additional methods, resulting in balanced accuracies ranging from 69% to 92%. This is the first study using a neural network model to focus on a rare genetic syndrome using phonation analysis data. By using synthetic data (oversampling and undersampling) and a CNN, it was possible to detect SMS with high levels of accuracy. Voice analysis and deep learning techniques have proven to be a useful and noninvasive method. This is a finding that may help in the complex identification of this syndrome as well as other rare diseases. Full article
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