A3S: AI-based Asthma App using Spirometer
Period
July 2024 – December 2026
Objective
AsthmaTuner is an existing remote digital tool owned by MediTuner AB in Sweden. AsthmaTuner consists of a handheld spirometer connected to a mobile app that can be used by patients with asthma and other respiratory diseases to diagnose and manage their health condition.
Explainable Artificial Intelligence (XAI) has high potential to enhance existing digital tools previously developed without AI or with black-box AI. The existing AsthmaTuner system does not use any AI so far, and naturally the scope of using black-box AI has a high potential. In this proposed project, we pursue a significant step ahead. We will explore how explainable AI (XAI) will enhance both the diagnostic and management capabilities of the AsthmaTuner system. That means, XAI will help both patient users and their health care providers further improve asthma care, with the target of reduced need for unnecessary health care visits, or costly and potentially deadly asthma attacks or exacerbations. The XAI-based digital tools are expected to prevent further health disparities and will include the addition of social determinants of health to the design, implementation, and evaluation to AsthmaTuner to improve the performance.
Background
Asthma is a large-scale health care problem, affecting around 10% of the European population, and moreover; asthma and other respiratory diseases are increasing due to environmental deterioration worldwide. Asthma is a complex problem, which is characterized by airway inflammation and respiratory symptoms of wheezing, dyspnea, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation. Potentially effective ways to address asthma care are to better understand the day-to-day lung function (a longitudinal data) and asthmatic symptoms of individuals and more effectively identify, diagnose, and manage asthma by exploring potential disparities in symptoms, lung function, and outcomes that may exist by social determinants for patients. AsthmaTuner (AT) is the existing, validated digital tool in this regard.
The general purpose of the project is to design an explainable AI (XAI)-based smart AT system that uses a mobile app and a handheld spirometer. We refer to the proposed project as “A3S: AI-based Asthma App using Spirometer”. For the A3S system development, XAI algorithms will perform patient data analysis and guide decisions. The term “explainable” in XAI refers to the ability of these algorithms to provide transparent and interpretable explanations to patients and health care professionals for their predictions or decisions. The XAI algorithms will provide person-centred interventions precluding the onset or minimizing the risk of asthma in disadvantaged groups. It will help to improve respiratory health by training person-centred AI-support in asthma diagnosis and management of asthma in disadvantaged groups based on social determinants. Overall, the A3S project addresses a pressing health care problem with potentially widespread impact.
Cross-disciplinary collaboration
This is a highly collaborative project with a team of data scientists, clinicians, and AI specialists. Partners are PI Magnus Jansson, Co-PIs Saikat Chatterjee, PostDoc Zhendong Wang of EECS KTH, Ioanna Miliou of DSV SU, Docent Björn Nordlund of KUH and KI and his team, along with MediTuner AB.
Contacts
Magnus Jansson
Professor at KTH EECS, PI: A3S: AI-based Asthma App using Spirometer, Digital Futures Faculty
+46 8 790 84 43janssonm@kth.se
Saikat Chatterjee
Associate Professor, Division of Information Science and Engineering at KTH, Main supervisor: Explainable Machine Learning for Early Warning Systems, Co-PI: Data-Limited Learning of Complex Dynamical Systems - Impact and Demonstrators, Former Co-PI: Data-Limited Learning of Complex Dynamical Systems, Co-PI: A3S: AI-based Asthma App using Spirometer, Digital Futures Faculty
+46 8 790 84 78sach@kth.se
Ioanna Miliou
Senior Lecturer, Department of Computer and Systems Sciences at Stockholm University, Co-PI: AI-based Asthma App using Spirometer, Digital Futures Faculty
+46 8 161608ioanna.miliou@dsv.su.se
Björn Nordlund
Adjunct Senior Lecturer | Docent, Karolinska Institutet, Project: A3S: AI-based Asthma App using Spirometer
bjorn.nordlund@ki.se