To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.

Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.

Computer Methods and Programs in Biomedicine is the companion title to the open access journal Computer Methods and Programs in Biomedicine Update.

Special Issue on In silico approaches to tackle coronary artery disease: where we are, where we are going
Submission Date: 2024-09-01

Digital twins are poised to provide cardiologists with a deeper understanding of coronary artery disease (CAD) pathophysiology and better decision-making support in the coming years. Specific tools based on in silico models are already applied as technology supporting cardiologists, who demonstrated a marked interest in integrating digital twin technologies into daily CAD management. These circumstances suggest that the time is ripe for the clinical translation of in silico models, promoting them from pure research methods to “in silico cardiology” technology.

However, the full exploitation of in silico models in cardiology is still hampered by several issues, including the intrinsic challenges of multiphysics/multiscale problems and not consolidated standardization protocols. These aspects are crucial to improve the reliability and the clinical impact of in silico models. Another challenge concerns the demanding computational costs to run simulations, often incompatible with clinical examination time. New approaches based on reduced order models and artificial intelligence algorithms are under development to supplement/replace conventional in silico strategies.

The present Special Issue aims at taking a picture of the state-of-art on in silico approaches to tackle CAD, focusing on the following topics:

    Novel approaches to coronary in silico patient-specific modeling
    Innovative mathematical/numerical methods for coronary simulation runtime speed-up
    Efforts for in silico modeling standardization and verification, validation, and uncertainty quantification of computational modeling assumptions
    AI-based techniques for coronary flow investigation and CAD diagnosis/management

The need to take stock of the situation and individuate new research lines promoting the clinical utility of in silico-based technology is essential for the definition of a road map which, in the next decade, will lead to the widespread application of the “in silico cardiology”.

Guest editors:

Dr. Giuseppe De Nisco
Polytechnic of Turin Department of Mechanical and Aerospace Engineering
giuseppe.denisco@polito.it

Dr. Maurizio Lodi Rizzini
Polytechnic of Turin Department of Mechanical and Aerospace Engineering

Dr. Alessandro Veneziani
Emory University

Professor Alison L. Marsden
Stanford University

Manuscript submission information:

You are invited to submit your manuscript at any time before the submission deadline 1st September 2024. Please select “VSI: In silico approaches to tackle coronary artery disease” as your article type. For any inquiries about the appropriateness of contribution topics, please contact Dr. Giuseppe De Nisco via giuseppe.denisco@polito.it

Keywords:

Coronary hemodynamics, computational fluid dynamics, fluid-structure interaction, artificial intelligence, machine learning, uncertainty quantification, validation, modelling standardization, modelling assumptions, coronary simulation runtime.

Special Issue on Secure, smart, and high-performance healthcare systems based on the Internet of Medical Things
Submission Date: 2024-09-20

Recently, human lifestyle has been greatly altered due to factors such as sleeping patterns, longer sitting postures, unhealthy eating habits, and poor work-life balance. To counteract the first degradation of human health, various health monitoring platforms and systems have been develop in order to support the continuous and real-time health monitoring.The goal of the proposed special issue is to advance the field of Internet of Medical Things (IoMT)-enabled health systems to support the delivery of safe and reliable healthcare using Machine Learning and Deep Learning technologies along with the adoption of novel and reliable High Performance Computing approaches.

Guest editors:

Assoc. Professor Jose Santamaria
University of Jaen, Department of Computer Science

Dr. Antonio Abad Civit 
University of Seville

Dr. Laith Alzubaidi
Queensland University of Technology

Dr. Priyanka Singh
SRM University AP

Dr. Hiren Kumar Thakkar 
Pandit Deendayal Energy University

Dr. Gracia Ester Martín Garzón
University of Almeria

Manuscript submission information:

You are invited to submit your manuscript at any time before the submission deadline 20 September 2024. Please select “VSI: Secure, smart, and high-performance healthcare systems” as your article type. For any inquiries about the appropriateness of contribution topics, please contact Assoc. Professor Jose Santamaria via jslopez@ujaen.es 

Keywords:

Healthcare; Internet of Medical Things; Data Privacy; Machine Learning; Deep Learning;

Special Issue on Exploring the Frontiers of Radiomics: Unveiling Novel Insights through Advanced Techniques and Multimodal Approaches
Submission Date: 2027-03-30

Radiomics is a quantitative approach to analyzing medical images in combination with molecular, genetic, and clinical information, which has evidenced very promising results especially in the field of oncology. Radiomics has rapidly evolved into a powerful tool for non-invasive disease diagnosis, prognosis prediction, and treatment response monitoring.

This Special Issue aims to gather recent advances and novel contributions from academic researchers and industry practitioners in radiomics research, shedding light on the potential of this burgeoning field to revolutionize personalized medicine. Review or summary articles (e.g., a critical evaluation of the state of the art or insightful analysis of established and upcoming technologies) may be accepted if they demonstrate academic rigor and relevance.

Submissions are encouraged to explore various aspects of radiomics, including but not limited to:

    Secure & Privacy-Preserving AI driving collaborative radiomics model construction: designing and implementing AI algorithms that learn diagnostic predictive models through federated and privacy-preserving methods. This would safeguard sensitive patient data, enabling shared knowledge while upholding individual privacy, revolutionizing non-invasive diagnostics in radiology.
    EXplainable Artificial Intelligence (XAI) for Radiomics: Utilization of XAI techniques and XAI algorithms for providing interpretable and transparent AI models that elucidate the intricate relationships between radiomic features and clinical outcomes and bridge the gap between complex AI-driven predictions and actionable clinical understanding.
    Advanced Feature Extraction: Novel algorithms and methodologies for extracting robust and discriminative features from medical images across different modalities, scales, and dimensions.
    Multimodal Fusion: Investigations into the integration of radiomic features from multiple imaging modalities (e.g., MRI, CT, PET) to enhance diagnostic accuracy and provide a comprehensive understanding of the underlying pathology.
    Clinical Translation: Studies focusing on the clinical implementation and validation of radiomic models, assessing their real-world utility and impact on patient outcomes.
    Radiomics in Precision Oncology: Investigations into the application of radiomics in cancer diagnosis, treatment planning, and monitoring, with an emphasis on tailoring therapies to individual patients.
    Radiogenomics and Radiomics-Pathology Correlation: Research bridging the gap between radiomics features, genomic data, and histopathological findings to uncover hidden relationships and enhance disease characterization.
    Quantitative Imaging Biomarkers: Development and validation of quantitative radiomic biomarkers for assessing disease progression, treatment response, and prognosis.
    Open Source Tools and Datasets: Sharing of open-source software tools, libraries, and annotated datasets to foster collaboration and reproducibility in radiomics research.

Guest editors:

Prof. Dr. Giancarlo G. Fortino
University of Calabria, Rende, Italy

Dr. Antonella Guzzo
University of Calabria, Rende, Italy

Professor Filippo Molinari
Politecnico di Torino, Turin, Italy

Professor Ye Li
Shenzhen Institutes for Advanced Technology, Shenzhen, China

Prof. Karen Panetta
Tufts University, Medford, MA, USA

Prof. Maria Francesca Spadea
Karlsruhe Institute of Technology, Karlsruhe, Germany

Manuscript submission information:

You are invited to submit your manuscript at any time before the submission deadline 30 March 2027. Please select “VSI: AI RADIOMICS” as your article type. For any inquiries about the appropriateness of contribution topics, please contact Prof. Dr. Giancarlo G. Fortino via giancarlo.fortino@unical.it 

Keywords:

Radiomics, Artificial Intelligence, XAI, Clinical Translation, Precision Oncology, Multimodal Fusion, Federated Learning, Radiogenomics