The Vector Institute, an independent, not-for-profit research institute focused on leading-edge machine learning, announced the second in its series of Pathfinder Projects to implement Artificial Intelligence (AI) in the health sector.
The second Pathfinder Project, performed in partnership with the University Health Network (UHN) and the University of Waterloo will enhance radiology diagnoses with AI.
Coral Review, a software solution developed at UHN, is a peer learning tool used by clinicians in diagnostic imaging to support continuous quality improvement of radiologist practice. Using an algorithm developed by Dr. H.R. Tizhoosh, Director of the Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) at UWaterloo and a Faculty Affiliate at the Vector Institute, an AI-enabled Coral Review would scan through thousands of existing medical images (i.e., x-rays) for ones similar to a patient’s and recommend a diagnosis to the attending physician.
“Coral Review currently enables anonymous peer reviews of medical imaging diagnoses. However, it is limited by the availability of physicians who perform the review or ‘second opinion’,” says Leon Goonaratne, Senior Director of Information Technology, UHN. “An AI-enabled peer review solution has the ability to provide the physician with more information when they perform the review, including the identification of images corresponding to rare or difficult to see cases”.
Pathfinder Projects are small-scale efforts designed to produce results in 12 to 18 months that guide future research and technology adoption. With technical and resource support from the Vector Institute, the projects each bring together a multidisciplinary research team to tackle an important health care problem or opportunity using machine learning and AI more broadly. Each project was chosen for its potential to help identify a “path” through which world-class machine learning research can be translated into widespread benefits for patients.
Dr. H. R. Tizhoosh and his team have worked at the nexus of health care and artificial intelligence (AI) for over a quarter century. Yet, only now is the world beginning to see the fruits of that labour. “In spite of the progress we’ve made,” he says, “we’re at the very beginning if we want to bring the technology into hospitals.”
Director of Kimia Lab at the University of Waterloo, Dr. Tizhoosh will be at the forefront of this important shift as he seeks to enhance University Health Network’s (UHN) medical imaging peer review system, Coral Review. It is the second of the Vector Institute’s Pathfinder Projects, which bring together multidisciplinary research teams to tackle important health care problems using machine learning.
Developed at UHN, Coral Review has been implemented at a number of hospitals across Ontario. Designed to bring focus to quality and education within medical imaging departments, the solution enables an anonymous peer review of a medical imaging diagnosis, as well as image quality.
“Coral Review has enabled a program of quality and education for many hospitals,” says Leon Goonaratne, Senior Director of Information Technology, UHN. “While this peer review process is helping identify and facilitate many learning and coaching opportunities across the province, we believe artificial intelligence is the next step to making the solution even more effective”.
To bring more regularity and efficiency into the system, Dr. Tizhoosh and his team are training a machine learning algorithm with a mixture of public and private data set of over 200,000 anonymized medical images. Once trained, the AI-enhanced Coral Review application would find similar looking images from past cases and offer suggested diagnoses, while leaving the final decision to doctors.
“It’s AI deployed in a slightly different way,” says Dr. Tizhoosh. “It allows the radiologist making the diagnosis to benefit from the knowledge of thousands of diagnoses made by other clinicians. That’s very different from making a diagnosis from scratch.”
The teams at UHN and Kimia Lab are starting relatively small, focusing on chest x-rays and specifically looking at pneumothorax, or collapsed lungs. The condition is a technical challenge for radiologists and a practical one for doctors; certain types can be difficult to see on an x-ray and a collapsed lung is both painful and potentially fatal. Small collapses pose a particularly significant challenge. “Doctors can miss small collapses in 40 percent of cases because you just can’t see it,” says Dr. Tizhoosh.
As it currently stands, their algorithm has about a 70 percent accuracy rate. But with technology and resources support from Vector they will fine-tune it over the next year and hope to push that rate above 90 percent before incorporating it into the existing system. Dr. Tizhoosh also hopes to expand the project’s scope beyond pneumothorax. “Long term, we want to add a long list of problems that we automatically check,” he says. “We want to find more difficult problems and work on a larger scale in the radiology domain.”
Once implemented, the system will be the first of its kind: an AI-enabled diagnostic tool for medical images based on image retrieval. “Working with hospitals to implement AI in medical imaging is the most thrilling thing I have ever done in my career,” Dr. Tizhoosh enthuses. “I want to look back and say, ‘this is what I did as a computer scientist.’ It’s a very exciting time.”
“Pneumothorax is a life-threatening emergency. It is typically detected by radiologists reviewing chest X-ray, however, long awaiting worklists will defer treatment”, says Dr. Antonio Szeto, a PDF at Kimia Lab, “Thus, an A.I. system automatically prioritizing X-rays with Pneumothorax for radiologists will benefit patients by reducing time to treatment. Supported by UHN and Vector Institute, we are now training an A.I. system based on Deep Neural Network from more than 500,000 chest X-ray images. It is an exciting project that potentially helps to save lives!”