Radiomics and Artificial Intelligence in Pulmonary Fibrosis.

A scoping review was conducted to investigate the role of radiological imaging, particularly high-resolution computed tomography (HRCT), and artificial intelligence (AI) in diagnosing and prognosticating idiopathic pulmonary fibrosis (IPF). Relevant studies from the PubMed database were selected based on predefined inclusion and exclusion criteria. Two reviewers assessed study quality and analyzed data, estimating heterogeneity and publication bias.

Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

DNA methylation of cytosine-guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non-methylated sites in a specific genetic region constitutes its methyl-haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes.

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool.

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles.

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks.

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images.

Towards Data Integration for AI in Cancer Research.

Cancer research is increasing relying on data-driven methods and Artificial Intelligence (AI), to increase accuracy and efficiency in decision making. Such methods can solve a variety of clinically relevant problems in cancer diagnosis and treatment, provided that an adequate data availability is ensured. The generation of multicentric data repositories poses a series of integration and harmonization challenges.

A sustainable HL7 FHIR based ontology for PHR data.

One of the most widely acknowledged standards in health informatics is HL7 (Health Level 7 International). HL7 FHIR® (Fast Healthcare Interoperability Resources) is a new HL7 standard for exchanging electronic health data. It builds upon previous HL7 data format standards, but also leverages more modern technical concepts and approaches, aiming to be more developer-friendly.