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Applied Sciences
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Application of Decision Support Systems in Biomedical Engineering
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Application of Decision Support Systems in Biomedical Engineering

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 622

Special Issue Editor

Dr. Maria Rizzi

E-Mail Website
Guest Editor
Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Interests: computer aided detection and diagnosis systems for biomedical signals; monitoring systems for health-care; analysis and synthesis of digital electronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the framework of healthcare facilities, the decision-making phase is frequent and of particular relevance to achieve results responding to expectations. The fusion of technology and medical science allowed for the development of decision support systems (DSSs) that are able to make good decisions in extremely complex and difficult medical diagnosis cases. Two different application contexts of DSSs can be configured, including the expert knowledge-provider context and the end user context. For example, a DSS could assist clinicians in solving diagnostic and therapeutic problems by making use of patient-related parameters as inputs which have to be combined with models and algorithms. The choice/adoption of a particular DSS depends on several criteria such as the complexity of the problem, the simplicity of the approach, the typology of data available, the expertise of the decision makers, and so on. A suitable and appropriate DSS has to be user-friendly and accurate.

This Special Issue addresses the most recent applications of decision support systems in biomedical engineering in the expert knowledge provider context and the end user context. Original high-quality papers and review articles are welcome and incentivized. In particular, topics of interest for this Special Issue include, but are not limited to, the following:

  • Decision support systems for biomedical systems;
  • Methods for the classification of biosignals;
  • Biosignal/bioimage feature extraction;
  • Algorithms to improve the quality of biosignals;
  • Artificial Intelligence tools for biosignal/bioimage analysis
  • Clinical decision support system;
  • Medical healthcare systems.

Dr. Maria Rizzi
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

  • advanced healthcare methods
  • biomedical image analysis
  • computer-aided detection systems
  • computer-aided diagnosis systems
  • monitoring systems

Published Papers (2 papers)

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Research

13 pages, 2867 KiB  
Article
Innovative Approaches to Clinical Diagnosis: Transfer Learning in Facial Image Classification for Celiac Disease Identification
by Elif Keskin Bilgiç, İnci Zaim Gökbay and Yusuf Kayar
Appl. Sci. 2024, 14(14), 6207; https://doi.org/10.3390/app14146207 (registering DOI) - 17 Jul 2024
Abstract
Background: Celiac disease arises from gluten consumption and shares symptoms with other conditions, leading to delayed diagnoses. Untreated celiac disease heightens the risk of autoimmune disorders, neurological issues, and certain cancers like lymphoma while also impacting skin health due to intestinal disruptions. This [...] Read more.
Background: Celiac disease arises from gluten consumption and shares symptoms with other conditions, leading to delayed diagnoses. Untreated celiac disease heightens the risk of autoimmune disorders, neurological issues, and certain cancers like lymphoma while also impacting skin health due to intestinal disruptions. This study uses facial photos to distinguish individuals with celiac disease from those without. Surprisingly, there is a lack of research involving transfer learning for this purpose despite its benefits such as faster training, enhanced performance, and reduced overfitting. While numerous studies exist on endoscopic intestinal photo classification and a few have explored the link between facial morphology measurements and celiac disease, none have concentrated on diagnosing celiac disease through facial photo classification. Methods: This study sought to apply transfer learning techniques with VGG16 to address a gap in research by identifying distinct facial features that differentiate patients with celiac disease from healthy individuals. A dataset containing a total of 200 facial images of adult individuals with and without celiac condition was utilized. Half of the dataset had a ratio of 70% females to 30% males with celiac condition, and the rest had a ratio of 60% females to 40% males without celiac condition. Among those with celiac condition, 28 were newly diagnosed and 72 had been previously diagnosed, with 25 not adhering to a gluten-free diet and 47 partially adhering to such a diet. Results: Utilizing transfer learning, the model achieved a 73% accuracy in classifying the facial images of the patients during testing, with corresponding precision, recall, and F1 score values of 0.54, 0.56, and 0.52, respectively. The training process involved 50,178 parameters, showcasing the model’s efficacy in diagnostic image analysis. Conclusions: The model correctly classified approximately three-quarters of the test images. While this is a reasonable level of accuracy, it also suggests that there is room for improvement as the dataset contains images that are inherently difficult to classify even for humans. Increasing the proportion of newly diagnosed patients in the dataset and expanding the dataset size could notably improve the model’s efficacy. Despite being the first study in this field, further refinement holds promise for the development of a diagnostic tool for celiac disease using transfer learning in medical image analysis, addressing the lack of prior studies in this area. Full article
(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)
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21 pages, 9113 KiB  
Article
Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
by Francesco Berloco, Pietro Maria Marvulli, Vladimiro Suglia, Simona Colucci, Gaetano Pagano, Lucia Palazzo, Maria Aliani, Giorgio Castellana, Patrizia Guido, Giovanni D’Addio and Vitoantonio Bevilacqua
Appl. Sci. 2024, 14(14), 6084; https://doi.org/10.3390/app14146084 - 12 Jul 2024
Viewed by 312
Abstract
Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a [...] Read more.
Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a sleep disorder characterized by the simultaneous occurrence of comorbidities. Survival analysis provides a potential solution for assessing and categorizing the severity of obstructive sleep apnea, aiding personalized treatment strategies. Given the critical role of time in such scenarios and considering limitations in model interpretability, time-dependent explainable artificial intelligence algorithms have been developed in recent years for direct application to basic Machine Learning models, such as Cox regression and survival random forest. Our work aims to enhance model selection in OSA survival analysis using time-dependent XAI for Machine Learning and Deep Learning models. We developed an end-to-end pipeline, training several survival models and selecting the best performers. Our top models—Cox regression, Cox time, and logistic hazard—achieved good performance, with C-index scores of 0.81, 0.78, and 0.77, and Brier scores of 0.10, 0.12, and 0.11 on the test set. We applied SurvSHAP methods to Cox regression and logistic hazard to investigate their behavior. Although the models showed similar performance, our analysis established that the results of the log hazard model were more reliable and useful in clinical practice compared to those of Cox regression in OSA scenarios. Full article
(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)
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