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.

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. 

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.

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.

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.