H2020Индивидуална стипендия2016–2018

INVeST · INdividual Vascular SignaTure: A new machine learning tool to aid personalised management of risk for cardiovascular disease

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2016-09-02 → 2018-09-01
Финансиране от ЕС: 183 455 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Съдовете в ретината на окото се анализират чрез машинно обучение, за да се създаде индивидуален профил на здравето на кръвоносната система. Това помага за по-точна ранна диагностика и персонализирано проследяване на риска от сърдечно-съдови заболявания.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

INdividual Vascular SignaTure: A new machine learning tool to aid personalised management of risk for cardiovascular disease

At the moment, the predictive accuracy of traditional risk factor estimates for cardiovascular disease (CVD), such as the Framingham model, the Prospective Cardiovascular Münster score and the European Society of Cardiology Systematic Coronary Risk Evaluation either over- or under-estimate the actual risk in a large number of individuals. Therefore, new biomarkers are currently being sought in order to offer a detailed profiling of individuals rather than population groups. Moreover, since most of these new biomarkers need complex assessments and assays, there is still a need for simple, reliable and non-invasive tests that correlate with the severity of CVD and that can be applied in primary screening settings. The “Individual Vascular Signature” (INVeST) project produced for the first time a method that creates in people at risk for heart disease, an individual vascular signature that is sensitive enough to aid early prediction of CVD, a personalised intervention as well as treatments follow-up Objectives: The overall main research objective of the INVeST project was to create a sound method that generates individual vascular signatures based on the measurements of the retinal vascular function. These signatures are used for early prediction of an abnormal vascular response in case of risk or pathologies and personalised intervention to modify it.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Health authorities in the EU have started to investigate the benefits of personalised health risk assessments and interventions in many diseases including cardiovascular disease (CVD), a worldwide leading cause of disability and mortality. Personalised health care aims at better diagnoses and earlier interventions, more efficient drug development and more effective therapies, thus providing better tools for clinical decision making and disease prevention.The INVeST project will produce for the first time a method that creates an individual vascular signature that is sensitive enough to aid early prediction and personalised intervention in CVD as well as treatments follow-up. INVeST will be able to provide the above through the following steps: (1) by participating in a short non-invasive eye test, any person can be provided with their own INVeST report, based on which lifestyle advice can be provided by a trained healthcare provider to improve their health and (2) if risk or CVD pathology is detected and intervention is applied, INVeST can help provide a progress report on the personalised intervention effectiveness and health improvements.The Fellow will (1) develop a mathematical model that describes each individual’s real-time retinal microvascular dynamic response to provocation, (2) identify and apply novel classifiers capable of accurately distinguishing between variation of healthy responses and pathological cases, (3) develop the mathematical apparatus, which based on the individual retinal vessel profile predicts the (individual) risk of developing CVD.INVeST brings together a talented young Fellow with background in computer science with three well-known groups in vascular imaging, computer science and health informatics. Following INVeST, with a portfolio encompassing personalised medicine, statistical model building, classification and CVD risk prediction, the Fellow will be an invaluable asset to the European multidisiplinary research community.

Оригинален текст от CORDIS (на английски).

Участници

ASTON UNIVERSITY · BirminghamКоординаторОбединеното кралство