Today, billions of light-based medical sensors are used by hospitals to measure quantities like blood flow, temperature, oxygenation and more. Clinical decision-making is partially based on the measurements from these sensors - so it’s important that these sensors measure data robustly. Unfortunately, the accuracy of light-based devices varies across demographics. Just as a soap dispenser may not always work for those with dark skin, a light-based medical device has fundamental challenges with signal-to-noise (SNR) ratio, and measurement accuracy. To solve this problem, and make devices more inclusive and even more accurate (for everyone), we need to rethink the sensing process. We draw from a paradigm of “equitable computational imaging”, where we co-design the optical sensing and computation layers to resist bias. Removing biases in both hardware and software, attacks the root causes of bias at the physical layer (e.g. light-based reflections). We will discuss new types of equitable imaging systems that measure heart rate and blood volume (contact-free and wirelessly); synthetic data pipelines that model melanin content; and theoretical results on dataset composition. By building novel sensors, simulators, and AI pipelines (together) we are able to demonstrate medical devices that - when deployed at UCLA’s Hospital - appear to reduce bias, and also improve accuracy (for everyone).
Achuta Kadambi received his PhD from MIT and joined UCLA as Assistant Professor in EECS, where he leads a computational imaging research group focusing on digital humans, computational imaging and equitable medical devices. He has received early career recognitions from NSF (CAREER), DARPA (YFA), and Army (YIP). He has also received the IEEE-HKN Outstanding Young Professional under 35 years old award, as well as other awards like the Forbes 30 under 30, Science. He holds 20+ US patents and has co-authored the textbook Computational Imaging (MIT Press, available as free PDF). He has also co-founded two computational imaging startup companies, one of which was acquired by Alphabet in 2022.