Digital Biomarkers

Digital biomarkers leverage data from wearable sensors and other digital sources to objectively assess health status and disease progression, aiming to improve diagnosis, treatment, and monitoring. Current research focuses on developing and validating these biomarkers using machine learning models, including convolutional neural networks, recurrent neural networks, and transformers, applied to diverse data modalities such as physiological signals (e.g., PPG, ECG), images (e.g., thermography, microscopy), and even smartphone-derived data. This field holds significant promise for personalized medicine, enabling earlier disease detection, more effective treatment strategies, and improved patient outcomes across various conditions, from cardiovascular disease to neurological disorders.