Ligne numérique

Students

RHHDS students, coming mainly from the natural sciences and engineering sectors, are also trained in ethical, legal and social implications of handling and analysing sensitive data.

  • Mathieu Baillargeon

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

  • Boby Lessard

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Louis Archambault
    Co-researcher
    Luc Beaulieu
    Start date
    Title of the research project
    Development of an automated calibration routine for multipoint scintillation detectors using the principal component analysis to represent the data
    Description

    Multipoint scintillation detectors are used to measure the dose of radiation deposited simultaneously at many locations in space and they have the advantage to allow real-time measurements. However, this detector must be precisely calibrated to provide accurate dose measurements.

    The goal of this project is to develop an automated routine for the calibration of multipoint scintillation detectors under the beam of a linear accelerator such as the ones used for cancer treatments, by representing the calibration data in the principal component space.

    A multipoint scintillation detector measures the spectrum of the light produced within the detector. Indeed, light is produced within the detector proportional to the radiation deposited in the detector. From a calibration dataset, a Non-Negative Matrix Factorisation algorithm (NMF) is used with the aim to retrieve the pure spectral components of the measurements. To simplify the visualization of the calibration dataset, the dataset is transformed using the Principal Component Analysis algorithm (PCA), and this transformed dataset is then represented graphically in the principal component space. This space allows to visualize the spectral composition of the data, relative to the pure spectra.

    Many datasets can therefore be built, represented into this space, and used with the NMF algorithm with the aim to evaluate the performance of this algorithm for different calibration datasets.

    In the end, this will allow to determine the experimental datasets that have to be acquired to perform an accurate calibration of the multipoint scintillation detectors.

  • Guillaume Jorandon

    Ph.D. candidate
    Faculté des études supérieures et postdoctorales
    Université Laval

    Directeur.e(s) de recherche
    Philippe Després
    Guillaume Latzko-Toth
    Start date
    Title of the research project
    Pseudo-medicine and data science: impact study of learning algorithms in the propagation of misinformation in health field
    Description

    This project studies the consequences of artificial intelligence (AI) systems and data science on public discourse, as well as their usage by the new content providers on the Web. 
    It will tackle the ethical aspects of learning algorithms and recommendation filters implemented by internet companies to select and present content to the user. Specifically, the project investigates the consequences of such algorithms on public health, especially in the propagation of medical misinformation and pseudo-medicine.

    This project aims at taking a critical oversight on data science techniques and their use. Various knowledge from different fields of humanities and social science will be applied (ethics, communication studies, philosophy of techniques) and will guide the development of technical solutions, as well as recommendations for the implementation of ethical and sustainable AI. 
    For this reason, we will need both technical and philosophical research, working towards interdisciplinary integration.
     

  • Felix Desrosiers

    Ph.D. candidate
    Faculté de médecine
    Université Laval

    Directeur.e(s) de recherche
    Vicky Drapeau
    Yves De Koninck
    Philippe Després
    Start date
    Title of the research project
    Design, operationalization and validation of a sustainable health evaluation model adapted to a digital platform
    Description

    The project focuses on the design, operationalization and validation of a sustainable health evaluation model.
    This model will be adapted to a digital platform and based on solid theoretical and conceptual foundations. Furthermore, it will gather valid indicators and will be supplied by data showing a global and ecosystem conception of health.
    Once operationalized, implemented and validated in a cohort study, this model will represent an innovative strategy for sustainable health through improved technologies and intervention methods.
     

  • Antoine Bouchard

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Philippe Després
    Start date
    Title of the research project
    Data pipelines in diagnostic radiology
    Description

    This project aims to create data pipelines in diagnostic radiology in order to supply analysis and visualization tools.

    The first pipeline is intended for data anonymization according to standard DICOM while the second one allows to supply Kibana (Elasticsearch) or Superset (Apache) platforms.

    The Airflow orchestrator (Apache) is used to automate the execution of pipelines which could eventually supply dynamic dashboards. 
     

  • Maelenn Corfmat

    Ph.D. candidate
    Faculté de droit
    Université de Montréal

    Student
    Directeur.e(s) de recherche
    Catherine Régis
    Anne Debet
    Start date
    Title of the research project
    The health data legal framework and associated medical liability mechanisms, within the artificial intelligence development framework : comparative European and North American prospects
    Description

    The research project is about the suitability of laws, legal principles and general framework surrounding health-related data, including those regulating the involved medical liability, in Canada and in the European Union. 
    It aims to identify its weaknesses and aspires to provide regulatory solutions that are more appropriate to the realities of artificial intelligence. These solutions should better balance private and public, individual, social, commercial and health-related interests at stake. Also, this project considers a different view of the law and of our current legal systems with missing satisfactory answers.
     

  • Angelika Kroshko

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Louis Archambault
    Start date
    Title of the research project
    Geometry-based quality control for external radiation therapy planning using stochastic frontier analysis
    Description

    This project focuses on the use of machine learning techniques in external radiotherapy for cancer treatment planning.
    Stochastic frontier analysis is a parametric approach used in econometrics and appropriated for medical physics. Using a retrospective bank of treated patients it will be possible to predict the optimal dose of radiation for tumor and healthy organs.
    This method is applied to multiple cancer treatment sites which emerge new challenge in the context of prediction, and data processing.

  • Sewagnouin Rogia Kpanou

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    François Laviolette
    Co-researcher
    Jacques Corbeil
    Start date
    Title of the research project
    Full characterization of drug-drug interactions using deep learning approaches
    Description

    The characterization of Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs
    co-administration. While considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The joint impact of the majority of combinations remains undetected until therapies are prescribed to patients. This raises the need for computational tools predicting DDIs in order to reduce experimental costs and exhaustively characterize all drug combination effects before therapy recommendations. 
    Previous attempts to build such tools focused on pharmacodynamic and pharmacokinetic interactions and used features that are difficult to access in the early stages of R&D. 
    In this work, we propose to use data about the drugs and their targets (pathways, biomarkers, gene expressions, etc) that are available at the beginning of each drug R&D campaign. Our hypothesis is that high-level deep learning features extracted from those data will improve DDI characterization. Therefore, our models will be trained to output the pharmacological effects of DDIs as well as underlying molecular and biological pathway interactions. 
    Creating such a comprehensive toolkit will help to reduce risks in polypharmacy therapies.
     

  • Daniel Gourdeau

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Louis Archambault
    Simon Duchesne
    Start date
    Title of the research project
    Hetero-modal synthesis of medical images using deep learning
    Description

    The research project is focused on the synthesis of medical images using deep learning, towards better artifact correction and the avoidance of unnecessary medical procedures.

    The neural networks designed in this project have a flexible architecture enabling the image synthesis from only an heterogeneous subset of input modalities. The images are synthesized in pathological situations, such as Alzheimer's disease and brain cancers.

  • Dylan Nazareth

    Undergraduate intern
    Communication Studies
    Concordia University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Incorporating patient and clinician voices into social media associated with a patient portal
    Description

    This project is centered on an examination of the process of preparing a patient-centered media and social media strategy that provides patients with useful information about the Opal patient portal and how they can make the most of it.

  • David Boghen

    Undergraduate intern
    Faculté des sciences
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Preparing a patient portal for use as a self-management tool
    Description

    This project is part of the effort to prepare Opal for use by patients, providing them with self-management resources such as questionnaires and educational materials.

  • Hossein Naseri

    Ph.D. candidate
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Use of natural language processing, radiomics and patient-reported outcomes to improve radiotherapy in cancer patients with bone metastases
    Description

    The primary objective of this research project is to detect cancer pain at an early stage by analyzing patients’ medical images. 
    Development of an algorithm to do this can be achieved by combining two computer science techniques: one that allows us to gather information about pain from medical notes, and one that extracts information from medical images. We will use the first technique in a computer program that will extract and quantify pain intensity recorded in patients' medical notes. 
    The second technique will be employed in another program that will analyze radiographic images of cancer patients’ to extract information about their bone metastases (such as tumor volume, and shape). Then, we will implement advanced statistical and mathematical techniques to model the relationship between identified tumor features and extracted pain intensities. 
    Finally, to validate our model, we will use pain scores that are directly collected from thousands of future cancer patients via a mobile app that has been developed in our group (opalmedapps.com).
     

  • Felix Mathew

    Ph.D. candidate
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Determination of the mutational signature of ionizing radiation using single-cell sequencing
    Description

    This research project aims to examine the mutational signature of ionizing radiation using single-cell sequencing techniques.

    The project is using human lymphoblastoid cells donated by the Ashkenazi trio that have a well characterized genome. The cells are irradiated and sequenced to determine the mutations that are induced as a result of the exposure to the ionizing radiation.

    Through biostatistical analysis of the human genomic data thus obtained, we will be able to identify the mutational signature of ionizing radiation.
     

  • Stacey Beard

    Undergraduate intern
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Asynchronous data federation for a multi-institutional patient portal
    Description

    A patient portal is an extension of an electronic medical record system that is accessible to patients. Although patient portals have been around for many years, they have had poor adoption in Canada. This is due in large part to the desire of provinces to invest in large centralized electronic medical record systems and the complexity of implementing such systems. But patients are demanding access to their medical data and do not wish to wait for complex centralized systems to be implemented.

    Therefore, in this research project, we will expand and evaluate the Opal patient portal, previously developed and implemented at the McGill University Health Centre, to function as a multi-institutional patient portal using a novel asynchronous data federation infrastructure.

  • Roxanne Caron

    Undergraduate intern
    Faculté de droit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Undertaking a Privacy Impact Assessment for a novel data donation platform based on the Opal patient portal
    Description

    This project is centered on the data donation aspect of Opal and will involve a Privacy Impact Assessment of the application and the eventual practice of data sharing driven by it.

  • Briana Cabral

    Undergraduate intern
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Preparation of the Opal patient portal for widespread use at the Cedars Cancer Centre and for the addition of a caregiver component
    Description

    This project involves two components: (1) preparation of Opal for the caregiver functionality in which patients will be able to share some or all of their medical data with their caregivers, and (2) general content preparation for Opal.

  • Romina Filippelli

    Undergraduate intern
    Medical Physics Unit
    McGill University

    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Privacy and confidentiality requirements for the use of a multi-institutional patient portal in Canada
    Description

    This project involves an examination of the regulatory privacy and confidentiality compliance requirements for the use of a patient portal in various Canadian provinces.

    Romina also works as a member of the quality assurance team, the market research team, and assisted with deploying Opal in numerous clinics within the Cedars Cancer Centre.

  • Kayla O'Sullivan-Steben

    M.Sc. candidate
    Medical Physics Unit
    McGill University

    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Empowered Patients, Informed Research - A pilot project for radiotherapy data donation using the Opal patient portal
    Description

    This research project is focused on preparing a pilot project for the donation of radiotherapy data by radiotherapy patients using the Opal patient portal.

    This project is investigating ways in which patients can share their data and it will put in place the infrastructure for a demonstrative project.

  • Anton Gladyr

    M.Sc. candidate
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Blockchain infrastructure for data donation using the Opal patient portal
    Description

    This research project is focused on using blockchain or an alternative solution to provide security for data donation using the Opal app.

    It will put in place a demonstrative blockchain infrastructure, examining its challenges and drawbacks and proposing potential innovative solutions.

  • Haley Patrick

    Ph.D. candidate
    Medical Physics Unit
    McGill University

    Student
    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    Use of patient-reported outcomes and accumulated dose for accurate quantification of the dose-outcome relationship in hypofractionated prostate cancer radiotherapy
    Description

    This project is focused on determining if patient-reported outcomes are better correlated with actual dose delivered than with planned dose in prostate cancer patients receiving radiotherapy.

    The project will use daily cone-beam CT images to calculate the daily and total radiation dose delivered to patients, and the Opal app to collect their patient-reported outcomes. 

  • Élina Francovic-Fontaine

    M.Sc. candidate
    Faculté de médecine
    Université Laval

    Directeur.e(s) de recherche
    Jacques Corbeil
    François Laviolette
    Start date
    Title of the research project
    Development of an in-process quality monitoring technology for plants during pharmaceutical manufacturing using high throughput mass spectrometry coupled to machine learning approaches
    Description

    Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

    To this end, we must identify the factors that regulate the production level for each plant. We plan to measure a great deal of molecules, called metabolites, to determine the optimal conditions for the plants to generate the maximal amount of each product. Since the quantity of measurements is large, we will use machine learning to design an artificial intelligence capable of understanding and identifying the potentially highly complex patterns of metabolites and/or biomarkers that are correlated with the optimal production.

  • Elsa Rousseau

    Postdoc fellow
    Faculté de médecine
    Université Laval

    Student
    Directeur.e(s) de recherche
    Jacques Corbeil
    François Laviolette
    Start date
    Title of the research project
    Machine learning for digital diagnostics of antimicrobial resistance
    Description

    The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach). New and improved approaches for tackling AMR include better surveillance; rational drug use, different business model for generating antibiotics, innovation at all levels and most importantly a global approach.

    This transnational team grant proposal is tasked to apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. Specifically, we will develop machine learning implementation that can orient the selection of treatments by assessing the level of resistance, provide rational for the generation of novel antibiotics, and assist in the surveillance of human and livestock AMR around the globe.

    To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills with demonstrated expertise in machine learning applied to both genomics and metabolomics and AMR domain experts. Our transnational team has all the elements to be highly impactful and to continue collaborating well past the JPIAMR funding period.

    The complementarity of our expertise will help us to tackle the challenges ahead and ensure our continued success. 

  • Ronan Lefol

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Philippe Després
    Start date
    Title of the research project
    Personalized dosimetry in computed tomography imaging
    Description

    The aim of this doctoral thesis is to develop a tool able to automatically provide organs of interest segmentation in computed tomography images using machine learning techniques.

    This tool will then be used to calculate organ doses in order to establish personalized dosimetric records in medical imaging. Doses will be calculated using informations obtained from images, radiographic technique and GPU-based Monte Carlo dose calculation algorithm (GPUMCD). Automated pipelines will be implemented to process large amounts of data.

    The proposed tool provides a better evaluation of population exposure to ionizing radiation caused by medical imaging procedures.

  • Danahé LeBlanc

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Louis Archambault
    Frédéric Pouliot
    Start date
    Title of the research project
    Radical prostatectomy preoperative radiomic analysis to predict lymph node metastasis in high-grade prostate cancers
    Description

    Prostate cancer is the most common form of cancer in men in Canada.

    This research project aims to establish a prognosis for a patient suffering from prostate cancer as well as predict the final pathology, by predicting the presence of lymph node metastases, from a FDG PET-CT. Radiomic characteristics are defined as the process of quantitative extraction of usable high-dimensional data from medical images. These are biomarkers that are difficult to see with the naked eye, such as texture and intensity. The database is made up of 250 prostate cancer patients. After filtration, a subset of 331 radiomic characteristics was selected. The accuracy of the model is 74.5%. This corresponds to an increase in precision of 6% compared to a model trained on all the extracted characteristics.

    Ultimately, the algorithm will better predict the risk of recurrent prostate cancer and help improve methods and choice of treatment.

  • Mojtaba Safari

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Louis Archambault
    Start date
    Title of the research project
    Role of machine learning in prostate cancer magnetic resonance imaging radiotherapy target definition
    Description

    The clinical and economic burden of prostate cancer in Canada is substantial and is rising. It has been indicated that 1 in 7 men will develop prostate cancer during their lifetime, and another 1 in 27 will die due to the prostate cancer. However, only a part of prostate cancer cases is clinically important which make the prostate cancer case discrimination crucial to avoid over-treatment. Compared to ultrasound imaging method, advanced MRI modalities have demonstrated a better diagnostic accuracy and is becoming a clinical routine examination for patients at risk of having clinically significant prostate cancer. Even though the version two of PI-RADS has been recently published to facilitate MRI modalities application in prostate cancer, they still present limitations. For instance, variability is reported in terms of inter-reader agreement and diagnosis accuracy, mainly depend on reader experience.

    This project aims to find a machine learning based approach for predication and segmentation of intraprostatic lesions to better guide radiation treatment. For accomplishing this task, the most advanced MRI modalities including DTI-MRI and DWI-MRI along with the anatomical MRI modalities will be employed. From the quantitative MRI modalities several maps that enhance specific features of the lesion will be extracted. Then after, texture information of the MRI modalities and selected maps will be extracted. In this step machine learning methods will be employed for feature selection and classification purposes. Finally, the prostate cancer extension and its type are identified.

  • Gabriel Couture

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Philippe Després
    Start date
    Title of the research project
    Robust data pipelines in radiation oncology
    Description

    This project consists of establishing good practices in health data management and building a software infrastructure in order to apply them.

    We have developed pipelines that allow daily recovery of brachytherapy treatment data in order to calculate and store their dosimetric indices in a database dedicated to research. These indices are essential for planning radiotherapy treatments and for estimating their quality.

    The aggregation of these indices allows different researchers such as bio-statisticians and radiation oncologists to carry out studies on larger data sets.

  • Samuel Ouellet

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Philippe Després
    Start date
    Title of the research project
    Automated extraction pipelines in medical imaging
    Description

    The objective of this project is to extract a set of relevant data from the files produced by medical imaging devices.

    The process consists of building ETL (extract-transform-load) pipelines to make the data consumable for analysis and visualization. An example of analysis consists in observing the trend in dose administered to patients according to the establishment, protocol or device used, in order to possibly identify non-standard practices.

    The data extracted could also guide practice by making it possible to assess the relevance of certain studies, and thus to optimize resources in the health network.

  • Keven Voyer

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Philippe Després
    Start date
    Title of the research project
    Proof of concept for the development of a decision support tool allowing approval exceptional medications through artificial intelligence
    Description

    An exception drug is a drug that is not usually covered by the public drug insurance plan (RPAM). The measures implemented at the RAMQ for exceptional drugs allow the entire population to obtain coverage for certain drugs if they are used in compliance with the indications recognized by the Institut national d'excellence en santé et services sociaux (INESSS). Exception drugs are now a large and constantly increasing part of total spending on prescription drugs.

    For the RPAM, one of the ways to control this increase is to reimburse these drugs according to pre-established rules. Currently, the system automatically processes around 20% of requests while the rest are directed to a case-by-case analysis, which generates delays.

    This project is to help the business sector respond more quickly to requests for approval of exception drugs. A tool will be developed based on 15 years of data collected by the current system, and will aim to increase the number of requests processed automatically.

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Featured project

This project studies the consequences of artificial intelligence (AI) systems and data science on public discourse, as well as their usage by the new content providers on the Web. 
It will tackle the ethical aspects of learning algorithms and recommendation filters implemented by internet companies to select and present content to the user. Specifically, the project investigates the consequences of such algorithms on public health, especially in the propagation of medical misinformation and pseudo-medicine.

Read more