Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine.

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences.

Investigation of Machine and Deep Learning Techniques to Detect HPV Status.

Background: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models.

Methods: Fifty patients with histologically confirmed HNC were included.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in Lu-DOTATATE therapy.

Background: Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship.

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ prior to the imaging acquisition considering only personalized anatomical characteristics of any new pediatric patient.GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs.

HPC+ in the medical field: Overview and current examples.

Background: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers.