Opportunistic Imaging & Signal Fusion

Opportunistic imaging and signal fusion represent a frontier in digital health, where artificial intelligence (AI) is used to extract more value from routine clinical imaging, often without requiring new data collection. Rice and Houston Methodist researchers are advancing methods to analyze and combine medical signals in ways that enhance early detection, diagnosis, and personalized care.
Imaging for health is indispensable to providing precise, efficient, and compassionate patient care, ultimately enhancing overall health outcomes and quality of life. From classical imaging modalities such as optical and medical imaging to emerging ones like photoacoustics, Houston is developing next-generation devices and machine learning algorithms to push the boundaries of accuracy, form factor, and efficiency of medical imaging technologies. These new technologies empower physicians to better understand the human body, measure and treat diseases, and provide therapy monitoring, enabling timely and personalized treatment plans.

Dr. Meng Li at Rice University leads efforts using AI to analyze echocardiogram (echo) images with greater precision. His research focuses on training machine learning models to detect subtle structural and functional cardiac abnormalities that may not be visible to the human eye during standard interpretation. These opportunistically collected images, often part of routine care, are being repurposed through advanced analytics to provide deeper cardiovascular insights, supporting earlier intervention and improved patient outcomes.

Complementing this work is Dr. Ashutosh Sabharwal’s research at Rice University in AI-enabled computer vision and signal processing. Along with collaborators like Dr. Sadeer Al-Kindi at Houston Methodist, his lab is developing algorithms that fuse multiple imaging modalities, such as echo, CT, and wearable biosignals, with clinical data. This fusion creates a richer, more complete view of patient health, enabling more accurate risk prediction and better-informed clinical decisions.
By integrating opportunistic imaging with signal fusion, DHI is building scalable, real-world tools that don’t just enhance diagnostics; they also reduce the need for redundant testing, improving efficiency across the care continuum. These innovations are particularly valuable in cardiovascular and metabolic health, where early signals of disease often exist long before symptoms appear.

Together, this multidisciplinary approach highlights the power of AI to reimagine existing clinical data as a proactive tool, unlocking precision health at scale.