Enhancing Dementia Risk Mitigation: Standardized Digital Monitoring in Clinical Trials and Platform Design for At-Risk Elderly Individuals.

BACKGROUND: Dementia is becoming a significant public health concern, affecting approximately 130,000 individuals in Austria, whereby nearly 40% of the cases are attributed to modifiable risk factors. Multidomain lifestyle interventions have thereby demonstrated significant effects in reducing the risk of dementia. OBJECTIVES: The goal was to define an interoperability framework to conduct standardized monitoring in clinical trials for enhancing dementia risk mitigation. In addition, the identified standards should be integrated into the components of the project. METHODS: A step-by-step approach was used, where initially data collection, aggregation and harmonization was carried out with retrospective data from various clinical centers. Afterwards, the interoperability framework was defined including the prospective data that is gathered during a clinical trial. RESULTS: A guideline for integrating healthcare standards was developed and incorporated into the technical components for the clinical trial. CONCLUSION: The interoperability framework was designed in a scalable way and will be regularly updated for future needs.

LETHE: A Digital Intervention for Cognitive Decline.

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

Evidence-Based Mobile Wound Application to Support Professionals in State-of-the-Art Chronic Wound Treatment.

BACKGROUND: One of the most important tasks within wound management and the basis of successful wound care is a clear and accurate nursing documentation which can be created in various ways. However, this documentation is only partially standardized mainly due to the interdisciplinarity of this task. OBJECTIVES: The goal is to overcome interdisciplinary boundaries in home care between health professionals, and to increase the patient's motivation to actively participate in wound healing by providing consistent structure and guidance. In this paper, the development of a mobile wound documentation application is described. METHODS: Comprehensive literature reviews were conducted on state-of-the-art wound documentation as well as on machine learning frameworks, databases and mobile app development technologies. RESULTS: As a result, a mobile wound application with Flutter, SQLite and Tensorflow Lite was developed. During the development process interoperability was considered to enable future extensibility. CONCLUSION: Further development in respect of backend and API is planned, involving user field tests.

Skin Lesion Detection with Support Vector Machines on iOS Devices.

Automated wound detection has become a common issue in health care. A broad variety of image processing algorithms already exist, but they are very power consuming on mobile devices. Meanwhile the use of machine learning algorithms is on the rise and new frameworks have been developed to use these techniques with improved on-device-performance such as Apple Core Machine Learning Interface. In this paper, we evaluate the performance of libSVM for wound detection in practice.