The 不良研究所 Clinical Innovation Competition and Awards Ceremony took听place virtually on May 21, 2020. To watch the video-recording of the event,听.
And the 2020 top teams are:
Winner of the Hakim Family Innovation Prize
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Stenoa
Coronary artery disease (CAD) is the leading cause of death worldwide. Treatment strategies rely on subjective interpretation of coronary angiograms, resulting in overutilization of health care services. Stenoa employs a novel machine learning algorithm to provide operators with real-time insight on the severity of any lesion, offering reliable intraoperative decision-making. Stenoa promises to improve clinical outcomes for patients with CAD, obviating unnecessary, invasive, and costly interventions.
Jeremy Levett, MDCM 2023 Candidate, Faculty of Medicine and Health Sciences, 不良研究所
Founder and Chief Executive Officer, Stenoa
Marco Spaziano, MD, MSc, Assistant Professor of Medicine, Faculty of Medicine and Health Sciences, 不良研究所, Division of Cardiology, 不良研究所 Health Centre
Chief Medical Officer, Stenoa
Ivan Ivanov, PhD,听Chief Scientific Officer, Stenoa
Tomer Moran, HBSc(c), Faculty of Science, 不良研究所
Chief Technology Officer, Stenoa
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Winners of the听Marika Zelenka Roy Innovation Prize听
This year, two teams tied for this prestigious prize.
GyroClear
GyroClear is a biomedical company aiming to set a new standard for minimally invasive intra-abdominal and thoracic surgery with our protective sleeve that maintains a clear camera lens throughout procedures. Loss of visibility is a constant problem for surgeons during laparoscopic operations and they spend a significant amount of time simply cleaning the camera. Our device would eliminate the need for interruptions to clean the camera lens during operations.
Aiden Reich, M.Sc Candidate in Experimental Surgery, 不良研究所
Pierre-Paul Gallant, M.Eng in Healthcare Technologies Candidate, 脡cole de Technologie Sup茅rieure
Serdar Kayan, MBA, Concordia University
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MedSafer
Many older adults are now taking 5, 10 or 15 medications. Polypharmacy can cause side effects such as memory and balance problems. Deprescribing is a solution that requires a health care professional to review your medications and suggest which ones can be stopped. The process can be time-consuming and requires expert knowledge. MedSafer is an app that helps guide a 鈥渕edication check-up鈥� by providing scientific information on the harms and benefits, and instructions for safe deprescribing.
Emily McDonald, MDCM, MSc, FRCPC, Assistant Professor of Medicine, 不良研究所 Health Centre
Todd Lee, MD, MPH, FACP, Associate Professor of Medicine 不良研究所 Health Centre
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Winner of the MI4 Innovation Prize
MinutesToMRSA
The mission of MinutesToMRSA is to provide high-quality diagnostic equipment to North American hospitals for the rapid, inexpensive, and high-throughput screening of Methicillin-resistant Staphylococcus aureus (MRSA) in admitted patients.
Alexander Bevacqua, Bioengineering Candidate, 不良研究所
Domenico Lopez, Bioengineering Candidate, 不良研究所
Ali Najmaldin, Bioengineering Candidate, 不良研究所
Jiachi Ou, Bioengineering Candidate, 不良研究所
Congratulations also to our finalists:
PLAKK
Heart attacks and strokes are caused by the rupture of fatty deposits, called plaques, that build up in the arteries. Currently, the only method to identify stroke or heart attack risk is to measure artery narrowing caused by the plaque. However, this method is insufficient and leads to inappropriate treatment allocation. PLAKK uses artificial intelligence to improve the characterization of dangerous plaques, leading to better prediction, treatment, and prevention of heart attacks and strokes.
Stella Daskalopoulou, MD, PhD, Faculty of Medicine and Health Sciences, 不良研究所
Principal Investigator, PLAKK
Kashif Khan, BSc, 不良研究所 PhD Candidate, Faculty of Medicine and Health Sciences, 不良研究所
Chief Executive Officer, PLAKK
Karina Gasbarrino, PhD, 不良研究所 Postdoctoral Fellow, Faculty of Medicine and Health Sciences, 不良研究所
Chief Scientific Officer, PLAKK
Nicolas Bent, BSc, Chief Technical Officer, PLAKK
Robert Brown, PhD, Technical Advisor, PLAKK
Ubenwa Health
Ubenwa is an algorithm, currently deployed as a smartphone app, which analyses acoustic biomarkers in the cry sounds of a newborn to detect early signs of brain injury due to perinatal asphyxia. Perinatal asphyxia is one of the top three causes of neonatal mortality today. Many hospitals in low-resource settings lack specialists or equipment to rapidly screen patients. Ubenwa will increase access to screening as it is cost-effective, easy to use, fast and non-invasive.
Samantha Latremouille, PhD Candidate, Division of Experimental Medicine, 不良研究所
Clinical Lead, Ubenwa Health
Charles Onu, PhD Candidate, School of Computer Science, 不良研究所
AI Research Lead, Ubenwa Health
Innocent Udeogu, Software Engineering Lead, Ubenwa Health
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