This research investigates whether artificial intelligence can help non-specialist clinicians diagnose deep vein thrombosis using AI-guided handheld ultrasound devices. By enabling faster point-of-care diagnosis in GP surgeries, the project aims to reduce hospital referrals, improve accessibility for vulnerable patients, and help healthcare systems manage increasing clinical demand more efficiently.

This research develops a noninvasive method for continuously measuring blood pressure using arterial resonance. Inspired by the physics of vibrating guitar strings, the device gently stimulates arteries and measures their resonance frequencies with ultrasound. The resulting continuous blood pressure waveforms could improve diagnosis of cardiovascular disease without invasive catheterization procedures.

This research improves photoacoustic imaging, a technique that uses light-generated sound waves to visualize tissue oxygenation deep inside the body. By calibrating measurements using highly oxygenated arterial blood, the method overcomes longstanding accuracy limitations and avoids skin-tone bias, potentially improving early tumor detection and non-invasive monitoring of tissue health.