PREDICT-X-ACS · Personalised Risk Evaluation and Diagnosis in Cardiovascular Treatment for Acute Coronary Syndrome
„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“
- Период
- 2026-01-01 → 2028-06-30
- Финансиране от ЕС
- 256 643 €
- Участници
- 4
- Схема
- HORIZON-TMA-MSCA-PF-EF
Линиите свързват координатора с партньорите.
Накратко на български
Изкуствен интелект анализира данни от медицински изследвания, генетика и околна среда, за да персонализира диагностиката при остър коронарен синдром. Това помага за създаването на по-точни индивидуални планове за лечение и намаляване на смъртността от сърдечни заболявания.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Цел на проекта
The Predict-X-ACS project, led by Dr. Deepa Joshi under the supervision of Prof. Lalit Garg at the University of Malta (UoM), aims to address the global burden of Acute Coronary Syndrome (ACS), a major contributor to cardiovascular disease (CVD). ACS, which results from the sudden reduction or blockage of blood flow to the heart, remains a leading cause of morbidity and mortality worldwide. This research integrates diverse data sources, including medical imaging, genomics, clinical records, environmental factors, and biomarkers, into a transformer-based, AI-powered precision medicine approach to enhance ACS care. The primary objective of the project is to develop scalable, explainable AI models that improve the diagnosis, treatment, and personalization of ACS care by overcoming the limitations of single-modal, non-transparent diagnostic tools. Through this multi-modal methodology, clinicians will be equipped with actionable insights, facilitating more accurate, individualized treatment plans and better patient outcomes. The project aligns directly with Sustainable Development Goal 3 (Good Health and Well-being) by contributing to the reduction of ACS-related mortality and improving patient care through the use of advanced medical technology. It also supports Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by driving innovation in healthcare technology and promoting the application of cutting-edge AI methods in clinical practice. Continuous clinical validation and real-time feedback mechanisms ensure that the developed AI models are robust, adaptable, and ready for broad clinical implementation.
Оригинален текст от CORDIS (на английски).
Участници
Връзки
- Виж в CORDIS
- DOI: 10.3030/101211169
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e52fbee9ce&appId=PPGMS
Данни: CORDIS, © Европейски съюз