Recognising depression in cancer patients can be difficult, and it becomes even more challenging when they have limited interaction with hospitals. To address this, FAITH has created an AI-driven algorithm that can detect signs of declining mental health in daily life. This algorithm was trained using data gathered from clinicians and patients at two European hospitals over the course of a one-year trial, following an approved study design.

Using a simple smartphone app, more than 200 patients involved in the FAITH trials shared information about their mental health, quality of life, sleep patterns, and nutrition through clinically validated questionnaires. Alongside developing the data collection system, the tech team made sure the trial produced data of sufficient quality and quantity to train the algorithm effectively. As a result, a predictive, federated model was created to provide early warnings by identifying downward trends in mental health among cancer patients.

Ultimately, the FAITH algorithm compares depression markers with validated clinical questionnaires, offering early alerts to detect declines in mental health among cancer survivors after completing primary treatment. This process does not require in-person assessments, enabling patients to get timely attention from healthcare providers.

Watch the FAITH 3rd and Final video