While heart failure is an insidious condition that leads to millions of hospitalizations per year, these hospitalizations are not a spontaneous event. In fact, physiological changes emerge for patients as they deteriorate days or weeks prior to the ultimate hospitalization.
Thus, a “signal” is often present within the body that indicates that a heart failure patient is declining. Traditionally, this signal has been very difficult to capture. But with the emergence of clinical grade wearable sensors and artificial intelligence,PhysIQ presents a case study of applying their approved AI to detect deterioration early enough such that proactive intervention can head off clinical decompensation and hospitalization.
Every four seconds, someone in the US suffers from stroke and every 4 minutes one person dies from stroke. This statement means that 28.4 million Americans are affected by a cardiovascular health issue. Moreover, the economic burden of cardiovascular diseases is reported to be at $555 billion in 2016, according to the American Heart Association.
Artificial intelligence and machine learning techniques are introducing new ways to improve the process of clinical decision makings based on cardiology images for cardiologists. They also show promising possibilities that would open doors of precision into cardiovascular medicine and services that focus on prevention, detection, management and treatment of adult cardiovascular diseases