Cerebral vascular accident (or cerebral stroke) and its consequences for patients, the health system, families and society are not new problems but have an increasingly clear and growing impact on people of developing countries in the world, including Vietnam. The main idea of the project focuses on solving two basic bottlenecks in the process of caring for stroke patients: (1) optimizing early diagnosis and early prognosis when suspicious symptoms appear; and (2) individualizing the recovery process after sequelae have occurred. These are also urgent problems in reality in Vietnam and many countries around the world when many people with acute stroke come to the hospital late (causing them to miss the golden time for treatment to completely recover), as well as too many patients are struggling with both physical and mental consequences after a stroke.
The project’s solutions include: (1) Applying artificial intelligence solutions to enhance at-home detection of suspected stroke cases as well as to enhance the effectiveness of post-stroke rehabilitation in the community. (2) Apply precious heritage in the system of traditional exercises to enhance the effectiveness of recovery from both physical and mental sequelae, help patients integrate into life faster, improve quality of life and support the process of preventing secondary cardiovascular events later. The implementation of the project includes 2 phased approaches to stroke patients: (1) Approach immediately in the acute phase: to improve the effectiveness of early diagnosis (early detection of stroke) and early prognosis (predicting the possibility of severe progression) thanks to artificial intelligence identification solutions. (2) Approach in the sub-acute phase to improve the effectiveness of recovery from stroke sequelae through improved Tai Chi exercises using solutions to guide self-practice and adjust exercises for patients and caregivers right in the community/at home.
The project’s products, if successful, in addition to scientific articles, are expected to include prototype recognition software that meets research goals, suits practical needs and is ready for large-scale clinical trials, assessing practical applicability as well as assessing commercialization potential.