U of T Technology & Startup Explorer

Machine learning architecture offers a novel, high-fidelity means of assessing lung physiology during the performance of (monofrequency) oscillometry tests.

Pulmonary Embolism (PE) diagnosis using machine learning (ML) techniques on CT pulmonary angiography (CTPA) is transforming the field of medical imaging. PE is a life-threatening condition where blood clots block the pulmonary arteries, requiring timely and accurate detection to improve patient outcomes. Traditional training and development of ML models for PE diagnosis involve extensive annotation, which is resource-intensive. To make these applications practical, the team has developed optimized training strategies, model architectures, and deployment pipelines for efficient PE detection.

Scalable, portable diagnostics for bacteria detection, molecular diagnostics, and immunoassays through microchip-based optical, electrochemical, ion, pH, and temperature measurements.

AutoScribe uses hands-free speech recognition and Artificial Intelligence to help reduce time consuming manual clinical documentation, freeing clinicians to place their focus where it should be – on their patients.