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.

Based on thermal and optical sensing analysis of digital imaging, this technology allows for rapid measurement of photoplethysmogram (PPG), temperature, respiratory rate, and heart rate beyond conventional distance limitations (e.g. at distances more than 1m), with the ability to add other components (e.g. tissue oxygenation and blood pressure) in the future.

A DCE-MRI acquisition and reconstruction package that enables more accurate quantification of physiological parameters in larger volumes than any state-of-the-art method. By first performing a high-temporal (rapid), high spatial (hifi) acquisition of anatomical details in the image set, each time frame is reconstructed with a high degree of precision and accuracy.

Fibrosis, or scarring of tissue, is not limited to the skin after an injury. In many chronic conditions, such as hypertension, autoimmune lung disease, and alcohol abuse (affecting the heart, lungs, and liver, respectively), tissue becomes progressively fibrotic over time, a condition that often goes undetected but ultimately results in organ failure and even death. Unfortunately, there is no cure for late-stage fibrosis, and tissue biopsy remains the only reliable diagnostic option, despite the deficiencies of limited sampling and mis-sampling.  At the University of Toronto, our inventors have developed a non-invasive magnetic resonance imaging (MRI) compound that can be administered to the patient to “light up” fibrotic tissue, including newly developing scars, in 3D throughout the body, to find fibrosis at early stage when the opportunity to interrupt disease progression is the greatest.