Winners of the DT4REGIONS Ideathon announced
In autumn last year, the DT4REGIONS project in cooperation with the Helsinki-Uusimaa Regional Council organised four ideathon workshops for Artificial Intelligence Potential in Preventive Healthcare. The participants could submit their innovative ideas on how to use AI for preventive healthcare until the end of November 2022. Learn more about the ideathon here.
In December, the jury selected four winning teams who submitted proposals displaying particular impact and innovativeness. Each team was awarded EUR 2500.
Learn more about the winning teams, their challenges and ideas below.
Prevention of Diabetic Foot Syndrome
Team FAIDEN from Leiden University presented the idea of a user-friendly, AI-driven software that can be used by diabetic patients at home. It allows the patients to monitor and detect early signs of severe foot problems. Diabetic Foot Syndrome can have severe consequences if not detected early, and is a major cause for lower limb amputation in Europe and worldwide. Read more here.
Emotional Awareness for Better Mental Health
Emotional avoidance is a cause for distress, and can lead to severe mental health conditions. An app being developed by team Soil from Healthy Mind Tech in Denmark enables long-term self-assessment and monitoring of the patient’s own emotional style, assisting also medical health professionals more precisely compared to conventional medical questionnaires.
AI-Assisted Decision-Making Tool Kit for Nursing Homes
In elderly care, a decrease in functional ability can lead to hospitalisation, with a drastic impact on the quality of life of the patient. Even small changes in eating, sleeping, weight and body mass, or everyday chores might predict early decreases in functional ability, but they are difficult to detect. The team from Attendo in Finland proposed an AI algorithm that could be used as a “gut feeling” to create guidelines and an individual prognosis for changes in functional ability.
Helsinki Health Study Score for Forecasting Healthy Aging in Midlife Adults
Early detection of risky health-related behaviours already in midlife adults could be a potential intervention to prevent health decline later in life. Huge amounts of patient-related information are collected, but it’s poorly used in preventive health care. An efficient AI approach is needed to solve complex statistics, find latent time series from individuals and prognose distinct developmental patterns in a novel way, to identify new risk groups. The Helsinki Health Study Team from University of Helsinki proposed using broad survey data in AI to build a scoring system at the individual level.