Periodic Reporting for period 4 - FAITH (a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment)
Berichtszeitraum: 2024-01-01 bis 2024-06-30
People with cancer are four times more likely to take their own lives than those without the disease, with depression being a common comorbidity. Even without suicide risk, depressed cancer patients face higher mortality rates than non-depressed patients. Symptoms of depression often go unnoticed or are dismissed as part of the cancer experience, delaying diagnosis and treatment, resulting in significant suffering and reduced quality of life. Innovations in ICT have introduced tools to support patients with chronic illnesses like depression. These range from devices transmitting physiological data for remote evaluation to mobile apps targeting specific health challenges.
The FAITH project aimed to create a Federated Artificial Intelligence solution for monitoring the mental health of cancer survivors, addressing the mental health challenges that often follow treatment. The project sought to enhance well-being through continuous and objective mental health monitoring, addressing the limitations of self-reporting and periodic assessments.
Key project objectives included:
1. Define the scope and architecture of the FAITH framework: Establishing use cases, functional requirements, user stories, and a reference architecture.
2. Develop a Federated Learning framework: Creating a privacy-preserving system for training AI models on decentralized data without compromising patient confidentiality.
3. Establish a robust data management framework: Ensuring ethical, secure handling of sensitive data.
4. Implement a voice/NLP interface: Developing a voice analysis component to identify mental health markers.
5. Demonstrate real-world applicability and value: Validating the system through clinical trials, stakeholder engagement, and real-world testing.
6. Develop a sustainable business and exploitation plan: Securing long-term sustainability through commercialisation strategies..
In conclusion, the team successfully completed several key tasks:
• Finalised and validated the FAITH system: Development of the mobile application, the clinical interface, and the Federated Learning framework, which underwent rigorous testing and validation.
• Carried the clinical trials: The clinical trials were crucial for evaluating the real-world performance of the FAITH system. Trials across multiple hospitals generated substantial data for analysis.
• Developed an Explainable Artificial Intelligence (XAI) solution for clinicians: A clinician-facing XAI tool for interpreting results.
• Conducted data analysis and prepared publications: Analysing trial data and preparing scientific papers to share findings.
• Prepared an exploitation and sustainability plan: A White Paper and a business plan outlining future development strategies.
• Formed an active support cluster: Establishing the "Cancer Support – AI for Wellbeing" cluster to advance research and innovation, with membership of 13 peer research projects.
In conclusion, the FAITH project demonstrated the feasibility of a federated AI-driven system for monitoring the mental health of cancer survivors, setting the stage for continued innovation in AI-powered healthcare.
The FAITH project aimed to create a Federated Artificial Intelligence solution for monitoring the mental health of cancer survivors, addressing the mental health challenges that often follow treatment. The project sought to enhance well-being through continuous and objective mental health monitoring, addressing the limitations of self-reporting and periodic assessments.
Key project objectives included:
1. Define the scope and architecture of the FAITH framework: Establishing use cases, functional requirements, user stories, and a reference architecture.
2. Develop a Federated Learning framework: Creating a privacy-preserving system for training AI models on decentralized data without compromising patient confidentiality.
3. Establish a robust data management framework: Ensuring ethical, secure handling of sensitive data.
4. Implement a voice/NLP interface: Developing a voice analysis component to identify mental health markers.
5. Demonstrate real-world applicability and value: Validating the system through clinical trials, stakeholder engagement, and real-world testing.
6. Develop a sustainable business and exploitation plan: Securing long-term sustainability through commercialisation strategies..
In conclusion, the team successfully completed several key tasks:
• Finalised and validated the FAITH system: Development of the mobile application, the clinical interface, and the Federated Learning framework, which underwent rigorous testing and validation.
• Carried the clinical trials: The clinical trials were crucial for evaluating the real-world performance of the FAITH system. Trials across multiple hospitals generated substantial data for analysis.
• Developed an Explainable Artificial Intelligence (XAI) solution for clinicians: A clinician-facing XAI tool for interpreting results.
• Conducted data analysis and prepared publications: Analysing trial data and preparing scientific papers to share findings.
• Prepared an exploitation and sustainability plan: A White Paper and a business plan outlining future development strategies.
• Formed an active support cluster: Establishing the "Cancer Support – AI for Wellbeing" cluster to advance research and innovation, with membership of 13 peer research projects.
In conclusion, the FAITH project demonstrated the feasibility of a federated AI-driven system for monitoring the mental health of cancer survivors, setting the stage for continued innovation in AI-powered healthcare.
The FAITH project transitioned from concept to a functional federated AI solution for monitoring the mental health of cancer survivors, focusing on six primary objectives:
1. Defining the FAITH framework: The team identified use cases, user stories, and a reference architecture, ensuring alignment with user and technical requirements.
2. Developing the Federated Learning framework: A proof-of-concept system using the Flower framework was deployed on OpenStack, with automated setup via Ansible playbooks.
3. Ensuring data privacy and security: A robust framework was developed to securely handle patient data, supported by visualization tools for hospital data analysis.
4. Voice/NLP integration: A voice component within the app captured audio data for analysis as potential depression markers.
5. Real-world applicability: Clinical trials and usability studies validated the system's practical use, integrating modules for activity tracking, sleep monitoring, appetite tracking, and voice analysis.
6. Sustainability planning: A business and exploitation strategy was developed, including the CS-AIW cluster, which has grown to over 13 projects and 250 members.
The FAITH project successfully delivered a functional and validated federated AI system for monitoring the mental health of cancer survivors. Our key achievements include:
1. A privacy-preserving data management framework.
2. A mobile app with activity, sleep, nutrition, and voice data modules
3. A federated learning framework for decentralised data analysis.
4. A standalone component to process audio vocal data.
5. A clinical interface to visualise and analyse patient data.
6. A sustainability plan and open-source platform for long-term impact
7. Plans for exploitation of key assets including:
- the open source platform,
- mobile app and an additional 8 x components of the solution
(activity monitor, nutrition and appetite tracker, sleep tracking module, voice module, federated learning framework, advanced analytics, adaptive AI module, activity structure and performance, XAI)
8) Continued dissemination of the project and its results
- Promotion on social media, including regular blog posts
- Final conference organised by clinical partner Champalimaud - ‘AI and Smart Technology in Mental Healthcare for Cancer Survivors’
- Participation in AI brokerage event organised by SETU in Ireland
- Presentation of paper ‘Digital Oncology: supporting clinical trials with biomedical sensors and wearables in cancer treatment and post-cancer follow up’
The project showcased AI's potential to address mental health challenges in cancer survivorship, emphasizing ethical practices and sustainability. Its robust foundation supports future development and commercialization efforts.
1. Defining the FAITH framework: The team identified use cases, user stories, and a reference architecture, ensuring alignment with user and technical requirements.
2. Developing the Federated Learning framework: A proof-of-concept system using the Flower framework was deployed on OpenStack, with automated setup via Ansible playbooks.
3. Ensuring data privacy and security: A robust framework was developed to securely handle patient data, supported by visualization tools for hospital data analysis.
4. Voice/NLP integration: A voice component within the app captured audio data for analysis as potential depression markers.
5. Real-world applicability: Clinical trials and usability studies validated the system's practical use, integrating modules for activity tracking, sleep monitoring, appetite tracking, and voice analysis.
6. Sustainability planning: A business and exploitation strategy was developed, including the CS-AIW cluster, which has grown to over 13 projects and 250 members.
The FAITH project successfully delivered a functional and validated federated AI system for monitoring the mental health of cancer survivors. Our key achievements include:
1. A privacy-preserving data management framework.
2. A mobile app with activity, sleep, nutrition, and voice data modules
3. A federated learning framework for decentralised data analysis.
4. A standalone component to process audio vocal data.
5. A clinical interface to visualise and analyse patient data.
6. A sustainability plan and open-source platform for long-term impact
7. Plans for exploitation of key assets including:
- the open source platform,
- mobile app and an additional 8 x components of the solution
(activity monitor, nutrition and appetite tracker, sleep tracking module, voice module, federated learning framework, advanced analytics, adaptive AI module, activity structure and performance, XAI)
8) Continued dissemination of the project and its results
- Promotion on social media, including regular blog posts
- Final conference organised by clinical partner Champalimaud - ‘AI and Smart Technology in Mental Healthcare for Cancer Survivors’
- Participation in AI brokerage event organised by SETU in Ireland
- Presentation of paper ‘Digital Oncology: supporting clinical trials with biomedical sensors and wearables in cancer treatment and post-cancer follow up’
The project showcased AI's potential to address mental health challenges in cancer survivorship, emphasizing ethical practices and sustainability. Its robust foundation supports future development and commercialization efforts.
The FAITH project advanced digital mental health monitoring by deploying a mobile app in clinical trials, capturing behavioural markers of depression through smartphone sensors and user interactions. This continuous monitoring offers a richer understanding of patient well-being than traditional methods. By aligning digital biomarkers with validated clinical tools, FAITH created a framework that bridges new data sources with established practices.
The societal impact is significant, enabling earlier interventions and better outcomes through frequent, non-intrusive monitoring. From a healthcare perspective, this approach can reduce system burdens by optimizing resource allocation while enhancing patient care through timely, data-driven decisions. By addressing privacy and explainability concerns, FAITH highlights the transformative potential of AI in mental health care for cancer survivors, fostering long-lasting benefits for individuals and healthcare systems alike.
The societal impact is significant, enabling earlier interventions and better outcomes through frequent, non-intrusive monitoring. From a healthcare perspective, this approach can reduce system burdens by optimizing resource allocation while enhancing patient care through timely, data-driven decisions. By addressing privacy and explainability concerns, FAITH highlights the transformative potential of AI in mental health care for cancer survivors, fostering long-lasting benefits for individuals and healthcare systems alike.