Toward Predicting Peripheral Artery Disease Treatment Outcomes Using Non-Clinical Data

Peripheral Artery Disease (PAD) significantly impairs quality of life and presents varying degrees of severity that correctly identifying would help choose the proper treatment approach and enable personalized treatment approaches. However, the challenge is that there is no single agreed-on measure to quantify the severity of a patient with PAD. This led to a trial-and-error approach to deciding the course of treatment for a given patient with PAD. This study uses non-clinical data, such as biomechanical data and advanced machine-learning techniques, to detect PAD severity levels and enhance treatment selection to overcome this challenge. Our findings in this paper lay the groundwork for a more data-driven, patient-centric approach to PAD management, optimizing treatment strategies for better patient outcomes.