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.

Machine-learning based methods to improve traffic flow through adaptive signal control.

SiMLQ is a process mining solution that uses event data and machine learning to automate the representation, analysis, and optimization of business processes. This enables better allocation of resources, increased productivity, and reduced costs in service systems.

A software platform that leverages statistical inference and machine learning to make more informed decisions.

Monte Carlo Lights is a novel ATSC approach that combines state-of-the-art reinforcement learning and Monte Carlo tree search to optimize traffic flow.

High-performance software solutions and advanced algorithms for cryo-EM data processing.

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.

Invasive and non-invasive hardware and software for vital signs monitoring

Electronic Systems for Monitoring, Diagnosis, and Control of Physiological Disorders

The two inventions captured in this technology allow for the representation, learning, and interactive editing of color palettes through a novel interface for creating color themes.