Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.

Purpose: Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases.

Methods: The main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease.

Involving Patients into the LifeChamps Project: Preliminary Findings.

Patient involvement in research has been highlighted as a major requirement for the development of products and services that cover actual patients' needs. However, there has not been an agreement on a commonly used standard for patient involvement in research, at least not in the EU, partially because of lack of common terminology and implementation methodology. Within the standardization activities of "LifeChamps: A Collective Intelligent Platform To Support Cancer Champions", this qualitative study was developed to discover patients' views for their engagement in research.

A Smart Digital Health Platform to Enable Monitoring of Quality of Life and Frailty in Older Patients with Cancer: A Mixed-Methods, Feasibility Study Protocol.

Objectives: LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field.

Objectives: To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

Data Sources: Peer-reviewed scientific publications and expert opinion.

Conclusion: The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field.

Translation, cultural adaptation and pilot testing of a questionnaire measuring the factors affecting the acceptance of telemedicine by Greek cancer patients.

The emergency presented through the COVID-19 pandemic exposed the need to adopt remote, technology-driven solutions and make healthcare services more resilient. To do so, we need technological applications (i.e.

How Telemedicine Can Improve the Quality of Care for Patients with Alzheimer's Disease and Related Dementias? A Narrative Review.

: Dementia affects more than 55 million patients worldwide, with a significant societal, economic, and psychological impact. However, many patients with Alzheimer's disease (AD) and other related dementias have limited access to effective and individualized treatment. Care provision for dementia is often unequal, fragmented, and inefficient.

A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture.

Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project.

Objective: The objective of this study was to employ ensemble clustering and tree-based risk model approaches to identify interactions between clinicogenomic features for colorectal cancer using the 100,000 Genomes Project.

Results: Among the 2211 patients with colorectal cancer (mean age of diagnosis: 67.7; 59.

Network Analysis of the Multidimensional Symptom Experience of Oncology.

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy.

Learning from data to predict future symptoms of oncology patients.

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient's treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance.

Prioritizing medication safety in care of people with cancer: clinicians' views on main problems and solutions.

Background: Cancer care is liable to medication errors due to the complex nature of cancer treatment, the common presence of comorbidities and the involvement of a number of clinicians in cancer care. While the frequency of medication errors in cancer care has been reported, little is known about their causal factors and effective prevention strategies. With a unique insight into the main safety issues in cancer treatment, frontline staff can help close this gap.

Prioritizing problems in and solutions to homecare safety of people with dementia: supporting carers, streamlining care.

Background: Dementia care is predominantly provided by carers in home settings. We aimed to identify the priorities for homecare safety of people with dementia according to dementia health and social care professionals using a novel priority-setting method.

Methods: The project steering group determined the scope, the context and the criteria for prioritization.

Preventing delayed diagnosis of cancer: clinicians' views on main problems and solutions.

Background: Delayed diagnosis is a major contributing factor to the UK's lower cancer survival compared to many European countries. In the UK, there is a significant national variation in early cancer diagnosis. Healthcare providers can offer an insight into local priorities for timely cancer diagnosis.

Identification of priorities for improvement of medication safety in primary care: a PRIORITIZE study.

Background: Medication error is a frequent, harmful and costly patient safety incident. Research to date has mostly focused on medication errors in hospitals. In this study, we aimed to identify the main causes of, and solutions to, medication error in primary care.

Clinician-identified problems and solutions for delayed diagnosis in primary care: a PRIORITIZE study.

Background: Delayed diagnosis in primary care is a common, harmful and costly patient safety incident. Its measurement and monitoring are underdeveloped and underutilised. We created and implemented a novel approach to identify problems leading to and solutions for delayed diagnosis in primary care.

Deficiency in apolipoprotein A-I ablates the pharmacological effects of metformin on plasma glucose homeostasis and hepatic lipid deposition.

Recently, we showed that deficiency in apolipoprotein A-I (ApoA-I) sensitizes mice to diet-induced obesity, glucose intolerance and NAFLD. Here we investigated the potential involvement of ApoA-I in the pharmacological effects of metformin on glucose intolerance and NAFLD development. Groups of apoa1-deficient (apoa1(-/-)) and C57BL/6 mice fed western-type diet were either treated with a daily dose of 300 mg/kg metformin for 18 weeks or left untreated for the same period.

Scavenger Receptor Class B Type I Regulates Plasma Apolipoprotein E Levels and Dietary Lipid Deposition to the Liver.

Scavenger receptor class B type I (SR-BI) is primarily responsible for the selective uptake of cholesteryl esters (CE) of high-density lipoprotein (HDL) by the liver and other tissues. In the present study, we show that SR-BI-deficient (scarb1(-/-)) mice are resistant to diet-induced obesity, hepatic lipid deposition, and glucose intolerance after 24 weeks of being fed a western-type diet. No differences in energy expenditure or mitochondrial function could account for the observed phenotype.