AI, Smart Sensors, and New Predictors for COPD Management

Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality worldwide. Telemonitoring plays a significant role in the management of COPD since it enables healthcare providers to detect early signs of COPD exacerbations, allowing for prompt intervention, and potentially preventing hospitalizations. While existing telemonitoring approaches have shown promise in reducing hospitalization rates, further studies are needed to evaluate the impact of telemonitoring on quality of life.
Through the CICERONE project, we introduce a home telemonitoring system that gathers multimodal data. This study presents the first developments, including a mobile application, an environmental air quality monitoring system, and an electronic patient record that brings all heterogeneous data in one place. This app has been developed using a user-centered design methodology with a group of 31 participants and empowers patients through surveys, games, voice and cough sound recording, and integration with vital sensors (peak flow meter, smart wristband, and a scale). Collected data is transmitted to a centralized electronic record, enabling continuous patient monitoring.
Currently, the system is gathering data in an observational study with a group of COPD patients. Machine learning models will be trained and validated to enhance the early detection of imminent COPD exacerbations. We aim to contribute to the advancement of COPD management by developing tools that foster proactive and individualized care.