I am a Canadian and American board-certified radiologist, and a Professor of Radiology and Adjunct Professor of Computer Science at the University of Alberta. My clinical practice is as a pediatric and musculoskeletal radiologist with Medical Imaging Consultants, Canada's largest radiology partnership. I have previously co-founded two startup companies, including Clearwater Clinical (now Shoebox), and MEDO.ai (now part of Exo Imaging). MEDO focused on artificial intelligence analysis of ultrasound images.
I obtained a combined MD-PhD at the University of Calgary, supported by a CIHR MD-PhD Studentship. My PhD thesis in Biomedical Engineering assessed the 3D relation of torso surface and spinal deformity in scoliosis using artificial neural networks, an early form of deep learning, in the 1990's. I then underwent residency training in Diagnostic Radiology at the University of Alberta in Edmonton, followed by two subspecialty clinical fellowships: Pediatric Radiology at the Royal Children's Hospital (University of Melbourne) in Melbourne, Australia, 2009-10, and Musculoskeletal Radiology at the Massachusetts General Hospital (Harvard University) in Boston, USA, 2010-11. I now have a combined clinical and academic appointment in Radiology at the University of Alberta. I held the Alberta Health Services Endowed Chair in Diagnostic Imaging from 2011-2021, and I am now the only practicing physician to hold a Canada CIFAR AI Chair (2021-2026). I am proud to be a Fellow of the Alberta Machine Intelligence Institute (AMII), one of Canada's top three academic institutes for artificial intelligence research and application.
From 2011-2021 my research has centered on adult/pediatric musculoskeletal radiology, particularly the influence of anatomy and childhood development of joints on the development of adult morbidity such as premature osteoarthritis. Through my work on diagnosis and management of infant hip dysplasia, I became interested in technological advances that extend the reach of ultrasound: 3D ultrasound, and artificial intelligence ultrasound image analysis. I co-founded the Collaborative for Ultrasound Deep Learning (CUDL), and then in 2018 I co-founded MEDO.ai, a startup company using artificial intelligence to interpret point of care ultrasound. Our work is under the umbrella of the Northern Institute for Development of Ultrasound (NIDUS). The combination of artificial intelligence and handheld portable ultrasound represents a powerful tool to bring advanced medical imaging to the point of care, as the 21st century stethoscope.
I hold three positions which are complementary and synergistic:
I seek highly qualified candidates for an M.Sc. or PhD., in Radiology, Biomedical Engineering or Computer Science. You must have experience in computer programming (e.g., Python, ITK/VTK, Matlab). Please fill out the CONTACT FORM, or send an email with your C.V. to email@example.com.
Ultrasound + AI
Medical imaging takes pictures inside the body. Most medical imaging requires bulky and expensive hardware: refrigerator-sized X-ray machines or multi-million-dollar CT and MRI scanners. Ultrasound is different: it is harmless with no ionizing radiation, and images can be obtained using portable handheld probes which now cost just a few thousand dollars and are nearly the size of smartphones (e.g., Lumify,Clarius, Butterfly).
Unfortunately, because it uses sound waves which obey different rules than visible light, ultrasound images are confusing. For inexperienced users it is difficult to know how to take high quality images and how to interpret them. My research investigates how artificial intelligence (AI) can help.
With proper guidance, AI can learn from the experience of experts on thousands of previous cases to identify anatomic structures and pathology in ultrasound images. AI can be used to inform users when they have obtained diagnostic-quality images, and to suggest diagnoses.
My team is evaluating the combination of ultrasound+AI in various clinical problems to see which ones it can be most helpful in. Some US+AI solutions can save lives; we are currently working on lung ultrasound+AI for the Covid19 pandemic. Others can be commercialized; we now have US-FDA approval for a tool to detect hip dysplasia and another to assess for thyroid cancer.
Portable ultrasound enhanced by AI is a natural way to extend the reach of ultrasound, transforming medical imaging. This could eventually be the 21st-century stethoscope!
Ultrasound in hip dysplasia
Hip dysplasia affects 1-3% of all infants born, and leads to dislocated hips in severe cases and premature osteoarthritis in milder cases if missed. Current screening for hip dysplasia uses conventional 2D ultrasound, which is unreliable because the limited view of the hip it shows depends highly on the skill of the sonographer.
Since 2012 my research team has been performing 3D ultrasound scans of hundreds of infants suspected of having dysplastic hips. 3D ultrasound provides a much more complete view of the hip, which ought to lead to more reliable diagnosis of hip dysplasia. We are developing visual and quantitative ways to make this diagnosis from 3D data, as shown in this video.
In 2015 I co-founded the Collaborative for Ultrasound Deep Learning (CUDL). This was a multidisciplinary, multi-national team of researchers and clinicians who share a vision that advanced machine learning techniques can be used to analyze uploaded ultrasound images (2D or 3D) to help clinicians optimize diagnosis and management of medical problems ranging from hip dysplasia to soft tissue tumors, cardiac and atherosclerotic disease, and other musculoskeletal and solid organ diseases. I describe CUDL, which is partly funded by a grant from the Canadian Medical Association Joule initiative, in this short video. CUDL has more recently transformed into NIDUS (Northern Institute for Development of Ultrasound).
Arthritis Scoring Systems
Many forms of arthritis, previously thought to be inevitable consequences of ageing and untreatable, are increasingly recognized to be at least partly due to treatable inflammation. To decide in whom a treatment is likely to be successful, to test whether a new therapy is effective, and to understand the natural history of each form of arthritis, it is important to have measures of disease that are more objective than patient-reported pain. MRI allows reliable tracking of disease status and changes over time, especially when a semi-quantitative scoring system is applied by a trained user.
I work with Dr. Walter Maksymowych, a rheumatologist, and Dr. Rob Lambert, a radiologist, on initiatives applying a modern web-based approach to objective semi-quantitative grading of arthritis. We have applied the approach to spondyloarthritis (SpA), osteoarthritis of the hip (see video of HIMRISS) and knee (see video of KIMRISS), and are investigating its use in juvenile inflammatory osteoarthritis (JIA). Our current focus is on automating these tools, to bring objective assessment of arthritis to the millions of MRI of joints performed worldwide each year.
AI is a powerful tool that can be used inappropriately. Concerns regarding issues such as medico-legal liability, data privacy and confidentiality are increasingly important to address. I am a member of the Canadian Association of Radiologists Artificial Intelligence Working Group, a multi-disciplinary national group focused on identifying appropriate use and support for AI in medical imaging.
I was first author on a white paper on ethical issues in AI, and am co-author on multiple additional manuscripts on these topics, which are quickly becoming some of my most-cited papers. This field is advancing at an appropriately rapid pace.