Predicting Scleral Lens Rotation Based on Corneoscleral Toricity

Patients with corneal disease often require treatment with scleral lenses. Unlike regular soft contact lenses, these lenses are much larger and have a space between the cornea and the lens that is filled with fluid before lens application. These lenses are extremely useful in cases of extremely ocular dryness and in patients with irregular corneas. Adjusting these lenses to perfectly mold the surface of the eye is of the utmost importance to ensure that the patient is comfortable and sees well with their lenses.

Autonomous structure detection and inspection using unmanned aerial systems

In this project, a new method is developed to optimize the performance of an Unmanned Aerial Vehicle (UAV) for autonomous detection and on-the-job view-planning of infrastructure elements with the purpose of their accurate three-dimensional (3D) modeling. The existing view-planning approaches in the literature have mostly modeled non-complex or small-scale objects and have rarely been adapted to flying robots. In addition, the target object is often identified by human operators.

AI to predict emergency visits is an AI-based predictive analytics platform that goes beyond traditional claims-based risk scores to use all patient-related healthcare data to provide both clinicians and care managers with a full breadth of timely, transparent and accurate predictions of health outcomes. helps value-based providers confidently answer a variety of health-care questions like, which patients are most likely to be readmitted to the hospital? Or which of my patients would most benefit from establishing a relationship with a primary care provider?

Link predicting in court

The company Lexum is an undisputed leader in the development of information retrieval tools for the law - statutes, regulations and decisions of courts and tribunals. The project is to improve a new tool offer by the company. The tool is used to retrieve a list of legal subjects from a factual description. With that list extract, the tool provides a list of potential related document.

Low data drug modeling

The project aims to facilitate the research and development of new drugs by exploring Machine Learning methodology useful for both the generation of new molecules and the prediction of molecule properties. Doing so will involve training deep learning models on a large number of small, heterogeneous datasets, with the objective of transferring learned representations quickly when faced with a new drug-discovery or drug optimization objectives.

Super resolution for MRI scans

Brain MRI scans are a critical component in the diagnosis of neurodegenerative disorders. However, there is a wide diversity in terms of the image quality and resolution obtained from different MRI scanner. In particular, it is common to find coarse resolution MRI scans (e.g. every axial slice is 3-5 mm thick), which limit the type of anatomical analysis that can be performed. The goal of this project is to develop and validate the performance of state-of-the-art super-resolution methods in 3D MRI scans, which generate high resolution MRI scans from low resolution scans.

Readmission AI: a predictive tool to assess patient risk of hospital readmission

Logibec Readmission AI is a predictive intelligence tool that accurately identifies the patient’s likelihood of being readmitted within 45 days of discharge. Rigorously and ethically developed with machine learning techniques in partnership with three large healthcare organizations in Québec, the predictive model uses reliable, accessible and timely clinical-administrative and sociodemographic data to provide clinically relevant stratification of readmission risk.