Research Projects

A cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease

Age Related Macular Degeneration is the leading cause of irreversible blindness in Canada and the industrialized world, yet there are no treatments for the vast majority of patients. Led by Tracery Ophthalmics inc, and working with Translatum Medicus inc (TMi) and academic partners at the Robarts Research Institute, Western University, and the “High Risk Dry AMD Clinic” of St Michael’s Hospital, we will engage SOSCIP’s Cloud Analytics platform, including servers, software and human resources, to accelerate the search for new treatments.

Specifically, Tracery has developed a novel functional imaging method, “AMD Imaging” (AMDI) that has already generated unprecedented pictures of the retina (the film of the eye) that include both known and unknown “flavours” of disease (the phenotype). These complex images will be compared against an individual’s genetic makeup (their genotype) and their concurrent illnesses, medications, and lifestyle history (their epigenetics). Further, Tracery’s imaging will help identify particular patients that will benefit from TMi’s drug development program, and ultimately help doctors choose which treatment will work best. Over the course of two years, we will involve increasing numbers of medical experts and their patients to generate and amass AMDI images, evaluating them over time and against other modalities.

Ultimately, through the “I3” program, we will work with IBM to train Watson and the Medical Sieve to recognize and co‐analyse complex disease patterns in the context of the ever‐expanding scientific literature. In short, we will leverage cloud‐based computing, to integrate image‐based and structured data, genomics and large data analytic to unite global users. We anticipate that this approach will significantly accelerate drug development, providing personalized treatment for the right patient at the right time.

Development of cardiac specific machine learning infrastructure

Analytics for Life, Inc. (A4L) is an early stage medical device company that specializes in the development of technologies to analyze patient physiological signals in order to evaluate cardiac performance, status and risk. A4L’s core competencies include identifying and developing mathematical features from physiological signals and assembling these features into clinically informative formulae using machine learning techniques. A4L has used third party machine learning tools (open source and licensed products) for the formula generation aspect of the product development cycle.

Specifically, A4L has used these tools to demonstrate the feasibility of computing left ventricular ejection fraction, cardiac ischemic burden and other cardiac performance/status parameters for simple to collect, non-invasive physiological signals (surface voltage gradients, SPO2, Impedance etc.). As a result of this experience, A4L have learned the benefits and insufficiencies of these tools for A4L’s specific purposes. A4L plans to file with the U.S. Food and Drug Administration (FDA) an application for approval of a physiological signal collection device and will soon afterwards be seeking market clearance for products assessing cardiac health emanating from the machine learning process. A4L believes it can build a machine learning tool specifically tailored for cardiac evaluation based on experience with the tools used to date.

This A4L-specific machine learning paradigm will search only relevant mathematical spaces, cutting down on time and CPU power needed to iterate to solutions and will allow for an assessment of a much wider array of potential solutions. Furthermore, this A4L-specific machine learning paradigm will provide a controlled and validated system that can be audited and evaluated by regulatory bodies, something that is not possible with the current machine learning tool(s). A4L proposes a hybridization of paradigms within a set mathematical space. This will create efficiency in the search, and therefore more searches can be performed in the same period of time. This will lead to more solutions being available for evaluation, resulting in more accurate and efficiently produced end solutions. If successful, this new paradigm will allow for simple, non-invasive, rapid and relatively inexpensive cardiac diagnostic capabilities, bringing tertiary care diagnostics to primary care settings and disrupting the current infrastructure and capital cost-centric model of diagnostic delivery.

Personalized predictive risk for medical imaging radiation exposure

This project will build the expert team and create the tools required to understand the long-term effects of low dose radiation exposure from medical imaging on populations and facilitate the adoption of best practices to decrease the impact of imaging related radiation exposure. Although this project will focus on low dose radiation exposure from medical imaging, the tools and approaches that will be developed will be transferrable to other specialties in the health care field.

To achieve these goals, we propose to:

(1) Develop a standard-based, extensible data model for a provincial platform to reconcile radiation dose with patient medical information;

(2) Develop generic data mining tools that can be customized to query Big Data repositories for use with major modalities, including Electronic Patient Records and Electronic Medical Records across the province;

(3) Investigate algorithmic approaches to extract and structure data from reports and text in DICOM headers, and from free text in notes and summaries in electronic patient records;

(4) Develop intelligent terminology mapping and quality assurance mechanisms to ensure data quality; and,

(5) Develop a feedback mechanism for decision support at the point of care related to imaging procedures.

Social media analytics for early detection of foodborne disease

Foodborne disease has emerged as a serious and underreported public health problem with high health and financial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major global public health threat in the twenty-first century. Traditional surveillance systems such as Canadian Notifiable Disease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborne illness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virology laboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days between case report from clinicians to the health departments. Public health decision-makers consider the delayed notification as a barrier to investigating foodborne disease, as it can potentially distribute geographically across great distances. Early detection of foodborne disease can reduce the number of exposed individuals by removing contaminated product from retail and foodservice outlets, increasing public awareness, and offering a more timely preventative and therapeutic measures to exposed individuals. We propose that social media data should be exploited as a complementary component of the traditional surveillance systems. The enormity and high variance of the information that propagates through large user communities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illness patterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of social media-based predictive models to characterize and detect upcoming foodborne illness outbreaks through ambient tracking and monitoring over users’ conversations in social media. The objective is to advance research on foodborne disease detection from non-traditional sources to supply health decision makers with situational awareness.

Spinal data analytics for computer aided diagnosis and prediction

A vast amount of information associated with spinal patient diagnosis, treatment and follow-up are generated every day. The application of multiple imaging tools (e.g. CT and MRI) has offered a great tool to diagnose and track diseases but at the same time poses significant challenges to human perception. One such challenge is the huge amount of information (a single Spinal Magnetic Resonance Imaging (MRI) is about 300M) associated with each scan. Often, a typical diagnosis and treatment of one spinal patient would involve multiple spinal imaging modalities and multiple follow-ups.

The ability to analyze changes in those data over time (longitudinal analysis) of all those data would provide much more accurate diagnosis and prediction. Despite the available data, there is a lack of automated tools to analyze the data to support physicians’ diagnosis, prognosis, and treatment. There is an urgent need of semi and fully-automated intelligent tools to help physicians with their quantitative measurement, longitudinal analysis, and population studies. To provide a comprehensive solution for the above challenges, we propose to develop a new generation of computer aided spinal data analytics system to improve the efficiency and accuracy of spinal patient care. This will be the first comprehensive cloud-based spinal diagnosis and prediction system, which will handle multiple spinal imaging modalities to facilitate detection, segmentation, direct historical diagnostic parameter extractions and medical records analytics. The automated data analytics will allow healthcare providers to manage the ever-growing amount of spinal data more effectively and positively contribute to immediate and long-term Canadian healthcare.