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Bioengineering
Special Issues
Machine Learning and Deep Learning Applications in Healthcare
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Machine Learning and Deep Learning Applications in Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2400

Special Issue Editors

Dr. Jorge Mateo

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Guest Editor
Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: artificial intelligence; machine learning; deep learning; medical decision support systems; biomedical diagnostic techniques; personalized medical treatments; molecular sciences; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Ana María Torres Aranda

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Guest Editor
1. Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
2. Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: machine learning; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is one of the research topics that attracts a lot of attention from healthcare system researchers. Compared to traditional machine learning methods, deep learning algorithms demonstrate their ability to train models from large datasets and images.

The computational capacity of deep learning models has enabled fast, accurate, and efficient operations in healthcare. Deep learning networks are transforming patient care and play a fundamental role in healthcare systems in clinical practice. Computer vision, natural language processing, and reinforcement learning are the most used deep learning techniques in healthcare. Moreover, these algorithms have significantly outperformed the performance of traditional methodologies for computer vision, natural language processing, robotics, and other fields.

This Special Issue on “Machine Learning and Deep Learning Applications in Healthcare” will focus on research works on the latest applications of deep learning in data analysis in different areas of healthcare research. The topics of interest for this Special Issue include, but are not limited to, the following:

  • Healthcare data analytics;
  • Medical imaging;
  • Deep learning in time series processing;
  • Biomedical diagnostic techniques;
  • Personalized medical treatments;
  • Predictive Modelling for Improving Healthcare;
  • Medical decision support systems;
  • Multimodal data processing and analysis for smart health;
  • Medical systems based on big data and artificial intelligence;
  • Artificial intelligence;
  • Other related topics regarding healthcare, deep learning, and biomedical engineering.

Dr. Jorge Mateo Sotos
Dr. Ana María Torres Aranda
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • machine learning
  • deep learning
  • big-data enabled healthcare systems
  • biomedical diagnostic techniques
  • diagnostic imaging
  • medical decision making
  • digital pathology
  • digital radiology
  • artificial neural networks
  • artificial intelligence
  • smart health

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Published Papers (2 papers)

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26 pages, 2769 KiB  
Article
Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis
by Ana María Cabanas, Nicolás Sáez, Patricio O. Collao-Caiconte, Pilar Martín-Escudero, Josué Pagán, Elena Jiménez-Herranz and José L. Ayala
Bioengineering 2024, 11(11), 1061; https://doi.org/10.3390/bioengineering11111061 - 24 Oct 2024
Viewed by 757
Abstract
Blood oxygen saturation (SpO2) is vital for patient monitoring, particularly in clinical settings. Traditional SpO2 estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed [...] Read more.
Blood oxygen saturation (SpO2) is vital for patient monitoring, particularly in clinical settings. Traditional SpO2 estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed 183 unique references from WOS, PubMed, and Scopus, with 26 studies meeting the inclusion criteria. The review examined AI models, key features, oximeters used, datasets, tested saturation intervals, and performance metrics while also assessing bias through the QUADAS-2 criteria. Linear regression models and deep neural networks (DNNs) emerged as the leading AI methodologies, utilizing features such as statistical metrics, signal-to-noise ratios, and intricate waveform morphology to enhance accuracy. Gaussian Process models, in particular, exhibited superior performance, achieving Mean Absolute Error (MAE) values as low as 0.57% and Root Mean Square Error (RMSE) as low as 0.69%. The bias analysis highlighted the need for better patient selection, reliable reference standards, and comprehensive SpO2 intervals to improve model generalizability. A persistent challenge is the reliance on non-invasive methods over the more accurate arterial blood gas analysis and the limited datasets representing diverse physiological conditions. Future research must focus on improving reference standards, test protocols, and addressing ethical considerations in clinical trials. Integrating AI with traditional physiological models can further enhance SpO2 estimation accuracy and robustness, offering significant advancements in patient care. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare)
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15 pages, 1434 KiB  
Article
Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance
by Miguel Suárez, Pablo Martínez-Blanco, Sergio Gil-Rojas, Ana M. Torres, Miguel Torralba-González and Jorge Mateo
Bioengineering 2024, 11(8), 762; https://doi.org/10.3390/bioengineering11080762 - 28 Jul 2024
Viewed by 1020
Abstract
Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other [...] Read more.
Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare)
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