Integrating Wearable Monitoring and AI to Predict ILD in Breast Cancer Patients Treated with Targeted Therapies

nterstitial lung disease (ILD) has emerged as a significant toxicity in breast cancer patients treated with novel targeted agents such as trastuzumab deruxtecan. This project aims to develop and validate an integrated monitoring and prediction model for ILD using multiparametric wearable devices (measuring oxygen saturation and heart rate), home spirometry, biochemical markers of inflammation and lung injury, and radiological data (CT imaging).

All collected data will be analyzed through advanced machine learning algorithms to identify early predictive patterns of ILD onset and progression. The ultimate goal is to create a clinically applicable risk stratification tool to guide treatment decisions, enable early intervention, and improve patient safety in real-world oncology practice.

We seek partners with expertise in biomedical engineering, data science, and AI model development, as well as collaborators from academic or industry backgrounds interested in translational oncology research.