Through this course, engineering students will be prepared interacting with robots and develop future innovations in biomedical robotics. The course covers the foundations of robotics for biomedical engineering. Students will learn about applications that range from biomedical lab automation, robot-assisted surgery, mobile and service robots in hospitals, as well as further smart robot types for healthcare purposes. The practical component of the course will allow students to interact and program collaborative robots in UTM's Robot Teaching Lab.

Students will learn foundational concepts of robotics, i.e., forward and inverse kinematics, dynamics, trajectory generation, motion planning and execution for serial robots. Further on, they will learn to program robot motions in a preplanned, teleoperated and collaborative robot-style fashion. They will be familiarized with state-of-the-art methods like active constraints, admittance control, as well as coordinate system transformations through point-based and image-to-physical registration. In their course project, students have the chance to develop a robot application that is centered around their own research project, towards a lab automation task or hard- and software extensions ranging from designing dedicated endeffectors, integrating sensors, or developing AI-based control methods.

Rehabilitation helps people overcome limitations in functioning, activities and participation to promote well-being. Salient challenges in rehabilitation include: ensuring equitable access to services, promoting engagement and sustained behaviour change, providing opportunities for home practice and self-management. Gaming technologies and innovations have immense potential to tackle these challenges. The design of effective gaming technologies for rehabilitation requires integrated knowledge of clinical practice, psychology, and technology, creatively applied. The evaluation of gaming technologies in rehabilitation requires careful consideration of common limitations, for example: small and heterogeneous samples, understanding and integrating divergent data sources. This course will introduce the concept of gamification in the context of rehabilitation. We will discuss topics relevant to the design, testing and translation of gaming technologies for rehabilitation including: introduction to gamification, co-design, and stakeholder engagement, theoretical frameworks, implementation science, and prototype testing with a focus on single case experimental designs and mixed methods. This overview course will introduce students to key theoretical and methodological concepts to support research and development of gaming technologies for rehabilitation.

This seminar/reading and conference course is an interactive course designed to provide graduate students a basic understanding of latest developments in cellular, gene-modified cells and gene therapy, articulating basic science concepts and challenges in process development, scalability, quality control, safety, potency, and meeting evolving regulatory requirements. Each week, we will invite a leading guest speaker to present a seminar on their respective field of research related to cellular and gene therapy, science, manufacturing and regulation. The course is divided into 4 sub-categories: MSCs, iPSCs, Immunotherapy, GMP/Regulatory considerations with 3 lectures on each subcategory. An Invited Speaker will present a seminar on his/her research for 45 minutes followed by an in-depth discussion on two papers assigned by the Guest Speaker. In addition to discussing the papers, the speaker and students will discuss the 2 written questions that each student submitted prior to lecture time. A total of two hours will be dedicated to each session, which will include the seminar by the Guest Speaker and a detailed discussion of an assigned paper and submitted questions, led by the Guest Speaker.

This course provides an opportunity to graduate students with both the breadth and depth in the area of machine learning and deep learning applied to biomedical applications. The course firstly introduces basic machine learning algorithms, including supervised learning (e.g., logistic regression, naïve bayes, decision trees, etc.) and unsupervised learning (k-means, hierarchical clustering). It will be followed by describing other fundamental concepts, such as classifier evaluation and statistical testing to compare classifiers. The next part is the study of different deep learning models (in seminar style) for various biomedical applications that deals with multiple types of data, including and not limited to biosignals, physiological data, environmental data, speech, text, images, and videos. Different types of supervised and unsupervised frameworks for sequential and non-sequential data will be discussed, including Feed-forward neural network, Convolution neural networks, Autoencoders, Long Short-Term Memory, Temporal Convolution Network. In the last part, advanced deep learning architectures applied to biomedical application will be discussed, including Generative Adversarial Network and Contrastive Learning. The course will comprise of programming assignment (2), paper critiques, and a group project (team of 1 to 3 persons).

This course will introduce various aspects of data science in digital health applications in the form of state-of-the-art projects. Students will build a foundation for performing applied data science, machine learning, and artificial intelligence (AI). The course will employ a combination of high-level theory with practical experience. Each team will work on a unique topic in biomedical engineering and will present their results in the form of a journal publication through merging all assignments and presentations during the course work. Therefore, students will achieve hands-on experience in how to perform literature review, data visualization, AI analysis and results interpretation as well as how to prepare a manuscript to publish their results and get reviewers' feedback on their work. Expert guest lecturers from researchers and scientists will be invited to present sample previous or ongoing projects in digital health. Teams of four to five students will choose their projects from the provided themes. This course will provide insights to help students apply theory to real world health examples targeting vulnerable population such as patients and older adults. This course will also provide students with lots of opportunities to use recent innovative sensing and vision technology for projects on prevention, detection, and treatment. We will discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into healthcare safely and ethically.

Cardio-respiratory disorders are among the most common chronic disorders and causes of hospitalization in adults, especially older adults. This course will describe the application of digital technologies to improve access to care and management of chronic cardio-respiratory disorders. It will help students to get a deeper understanding of several conditions, including sleep problems, heart disease, lung disease, and opioid's effect on respiratory control. For each condition, this course includes physiological and clinical descriptions of the condition and the recent advances in digital technologies to manage these disorders such as diagnosis and treatment options. This course also includes the basics of user-centered design and health equity as the foundation of developing technologies to improve access to care.

The goal of this course is to be able to understand the fundamental theories behind the development of biomedical products from idea to commercial release. At the conclusion of this course, students should be able to: 1) understand the theory behind the development of biomedical products from idea to commercial release; 2) apply the theory to critically analyze the relevant processes; 3) integrate the above knowledge with real world examples and solve practical problems; 4) deliver projects in a team through interactions and group projects; and 5) appreciate the translational link between the fundamental concepts of biomedical engineering knowledge and its practical application in the development of commercial medical products, the processing of such products and the design considerations for clinical use of such products. The main themes of the course are: developing proper requirements; design control; regulatory requirements; IEC 60601 medical device standard; risk management (ISO 14971); verification and validation.

The course will emphasize fundamental engineering principles that will allow students the ability to become productive team members and give them the background necessary to assume leadership roles in product development. Guest experts, case studies, and real world examples augment the learning experience. Each theme incorporates fundamental engineering principles that will allow you to work effectively in a medical device company or to bring your own product to market.

The objective of this course is to provide students with regulatory body and ethics considerations by which they engineer safe medical device products intended for use as implantable devices or in contact with body tissue and fluids. A top down approach will be taken where the regulatory path for product approval and associated costs with product development and validation are reviewed for different biomaterials and devices. This path is then assessed in the context of product specific reimbursement, ethics, safety, competitive positioning, and regulatory concerns. Students will be required to use their existing knowledge of biomaterials and devices, and their biocompatibility to frame the questions, challenges and opportunities with a mind to re-engineering products in order to capitalize on niche regulatory pathways. The resulting regulatory path gives a good idea of the kind of trial design the product must prevail in and ultimately the design characteristics of the device itself. Decision making will be made with ethical considerations. The discussion model will focus mostly on the United States regulatory office with some comments on Canada and Europe. Lastly, quantitative product development risks estimates are considered in choosing a product path strategy for proof of concept and approval of safe products. Ethical issues can also impact design since in biomedical engineering they are currently studied in the fields of bioethics, medical ethics, and engineering ethics. Yet, professional ethical issues in biomedical engineering are often different from the ones traditionally discussed in these fields as they need to align with the engineering profession. Biomedical engineers differ from medical practitioners, and are similar to other engineers, in that they are involved in research for and development of new technology, and do not engage in the study, diagnosis, and treatment of patients. Biomedical engineers differ from other engineers, and are similar to medical practitioners, in that they aim to contribute to good patient care and healthcare. The ethical responsibilities of biomedical engineers thus combine those of engineers and medical professionals, including a responsibility to adhere to general ethical standards in research and development of technology and to do R&D that adheres to the specific standards set forth by medical ethics and bioethics. This course focuses on products currently for sale as case studies, or may be approved for sale within the next two years consistent with its practical commercial focus.

This course will apply human factors engineering principles to the design of medical devices. Testing medical devices in a health care setting, with realistic users, will be emphasized to understand why devices fail to perform adequately. Students in this course will work in teams to complete an evaluation of a medical device design, existing prototype, or commercial product by conducting usability studies, with realistic users, to uncover use errors. Human factors engineering analysis will be used to propose and make design changes to improve the design and validation testing will be used to prove that design modifications yield a reduction in use-related errors. Throughout the course, topics will be covered as they relate to applicable medical device industry standards (e.g., quality and risk management of medical devices and usability and human factors engineering of medical devices) through lecture activities, examples, case studies, and the overarching design project.

A Practical Experience in Applied Research course in biomedical device development. The placement must be in at least one of the following biomedical engineering research fields: 1) Biomaterials, Tissue Engineering and Regenerative Medicine; 2) Engineering in a Clinical Setting; 3) Nanotechnology, Molecular Imaging and Systems Biology; or 4) Neural/Sensory Systems and Rehabilitation. The practical experience course can be taken in academic research and teaching laboratories, government institutions, health-care facilities, in the industry, or in health-care consulting firms.

A Practical Experience in Applied Research course in biomedical device development, usually over one session for a full-time placement. The placement must be in at least one of the following biomedical engineering research fields: 1) Biomaterials, Tissue Engineering and Regenerative Medicine; 2) Engineering in a Clinical Setting; 3) Nanotechnology, Molecular Imaging and Systems Biology; or 4) Neural/Sensory Systems and Rehabilitation. The practical experience course can be taken in academic research and teaching laboratories, government institutions, health-care facilities, in the industry, or in health-care consulting firms.

The final evaluation of the performance of the student will be conducted by an Evaluation Committee based on a formal Intern evaluation, the Summary report of the student's experiences for each internship period, and an oral presentation of the Summary report. Clinical Engineering Practice is the management of modern health care technology. This course provides practical experience in the practice of clinical engineering. Topics to be covered include in-service education, departmental management, equipment acquisition, equipment control, equipment design, facility planning, information systems, regulatory affairs, safety program, system analysis, and technology assessment/evaluation.

The 'Biopartnering' seminar series is a program require­ment for all MBiotech students — in both the BioPh and DHT streams. BTC1600H and BTC1610H are held in conjunction with one another, meaning all students (regardless of year or program stream) attend the seminar on the same date and time. The seminar is held once per week during the Fall semester, on Tuesday evenings for approximately two hours. It is comprised of both presentations by select speakers from industry as well as student presentations. The course challenges students to provide insights into industry issues that would be seen as a valuable contribution by experts in the area. Each student will participate in a formal group presentation, in their first year, and will complete other academic requirements such as critiques, team mentoring and an individual report in their senior year. The topics presented in this course will range from scientific (latest technologies and research, analysis of pre-clinical and clinical data) to business-oriented issues (e.g., market strategies for pharma and biotechnology products, government regulations, intellectual property, finance, ethics, etc.)

The 'Biopartnering' seminar series is a program require­ment for all MBiotech students — in both the BioPh and DHT streams. BTC1600H and BTC1610H are held in conjunction with one another, meaning all students (regardless of year or program stream) attend the seminar on the same date and time. The seminar is held once per week during the Fall semester, on Tuesday evenings for approximately two hours. It is comprised of both presentations by select speakers from industry as well as student presentations. The course challenges students to provide insights into industry issues that would be seen as a valuable contribution by experts in the area. Each student will participate in a formal group presentation, in their first year, and will complete other academic requirements such as critiques, team mentoring and an individual report in their senior year. The topics presented in this course will range from scientific (latest technologies and research, analysis of pre-clinical and clinical data) to business-oriented issues (e.g., market strategies for pharma and biotechnology products, government regulations, intellectual property, finance, ethics, etc.)

This laboratory-based course introduces fundamental experimental techniques commonly used in biomedical research and provides 'hands-on' experience working with nucleic acids and proteins over an intensive six-week schedule. Students receive a practical overview of key protocols over the first week and are provided with same-day, interactive technical demonstrations in a fully equipped 'wet' laboratory. This is followed by an extended research assignment in which students work in teams towards expressing and isolating a biomedically relevant, recombinant protein. Teams must design an appropriate research strategy, conduct experiments, collect and analyze data, and submit their product with a final report to meet a tight deadline. The course concludes with a final presentation seminar day.

This course is designed to enable students to gain a more in-depth appreciation and understanding of the application of materials science and protein chemistry to the field of biotechnology. We delve into advanced drug delivery and therapeutic strategies, biomaterials in medicine, pharmacology, and drug discovery. We also consider new disruptive technologies as case studies for life science biotechnology students.

As a companion course to BTC1710H, this laboratory course is intended to provide students with hands-on experience with some concepts in protein and materials chemistry. The experience will focus on the use of advanced equipment and techniques and will include experiments involving protein PEGylation, nano­particles in drug delivery, and biodiesel synthesis, as well as bioinformatics. This is an intensive four-week course, operating five days a week. Students will complete these projects and experiments in teams. A significant component of this course involves a science-intensive, business assessment in which the students have an opportunity to apply what they have learned.

This course will introduce students to the development of a wide range of product categories. While the focus will be on drugs, the course will also touch upon medical devices, digital health, big data in health, medical apps, biomarkers, medical marketing, treatment guidelines, screening tools and diagnostics. Understanding clinical trial design and the regulatory pathway through the US FDA is a major focus of the course. Reimbursement is introduced for both drugs and medical devices. Each year, this course is usually able to negotiate some major project opportunities from teaching hospitals students can tackle, to expose them to the clinical world, an important target customer environment of pharmaceutical companies.

This is a half-credit course that introduces students to some of the key aspects of the biopharmaceutical process, with special emphasis on the biotech sector. The course focuses on the fundamental role played by corporate entities in the development of new therapeutic drugs in a highly regulated business environment. Topics covered include biopharmaceutical manufacturing, regulatory approval for drug products and medical devices, setting regulatory standards, quality-by-design, cGMP compliance, risk management, and root cause analysis.

This course will focus on the exploration and understanding of biotechnology as applied to agriculture, natural products, biocontrols, and associated industrial biotechnology. Students will work in teams and each team will present their assigned topics as oral presentations and written assignments. A number of written individual assignments plus an exam will also be evaluated. In the agriculture area, lecture topics include modern approaches to plant breeding, genetically modified organisms (GMOs) and the controversy surrounding them; genomics and its importance in agribiotechnology; nutraceuticals; the use of natural and engineered products for pest and herbicide control; and the use of plants as bioreactors. In the natural products/­​biocontrols/­​industrial biotechnology areas, topics include the use of natural plant products for medicinal purposes; bioremediation of contaminated soils and the applications of biocatalysts as part of the green chemistry movement.

Medical device reimbursement for medical devices is critical for successful product commercialisation. This course discusses the regulatory and reimbursement landscape in Canada and presents a medical device reimbursement framework that can be applied when seeking medical device reimbursement. The framework focuses on the medical device and the reimbursement environment where payment is being sought. The course involves lectures and reimbursement challenges. Teams of students will conduct reimbursement assessments and develop and present reimbursement plans for real-world medical device reimbursement. At the conclusion of the course, students will have the knowledge and tools to build a medical device reimbursement plan.

This course teaches basic programming skills to non-programmers and introduces them to the value of those skills. Students will learn about the various capabilities of the R programming language and participate in discussions about the purpose of programming including task automation and interactive web design. Students will be introduced to elementary data types, control flow and functions as well as functional and object oriented programming. Students will practice approaches to problem solving with computer programs and learn debugging strategies. By the end of the course, students are expected to create a program that helps them solve a problem or perform a task (either self-chosen or assigned) in the context of data science.

This course will introduce students to biostatistics and data science. This course is intended for both students new to the area and those with prior training.

Statistical and data analysis methods covered will start with descriptive statistics and basic univariate tests and continue to more advanced regression models and other topics. The sessions will include lectures and hands-on tutorials that include real-time exercises. It is key that students are able to identify which methods to apply to what kind of data set, the assumptions of the model and how to interpret the output. Special emphasis in the course will be placed on critical thinking around analytical methods to be used.

Problem sets will be focused on the application of statistical modelling to the biological and health sciences. This may include laboratory or clinical data sets. Your defence of your analysis, as well as critiquing the work of others, will require you to draw upon some of your knowledge of biology and the health sciences.

A key component of the course will involve programming in R in order to conduct statistical analysis. Students will have both individual and team assignments to provide practice coding in R, one of the main languages used today in performing statistical analysis. Comfort with R will be helpful in learning other languages in the future in a statistical context. Off-the-shelf software, while more convenient, may not be available in the work environment you find yourself in and certain tests you may need, may not be available in any such software. Thus, learning to code is the best path forward for future practitioners of data science.

In this course, we focus exclusively on the dominant role of biologic therapies in modern medicine. In 2020, six of the top ten drugs by revenue were molecules of biologic origin, namely those manufactured primarily by biosynthetic rather than chemical means, with sales of the top selling therapy, the anti-TNF-alpha monoclonal antibody adalimumab, falling just shy of the US$20 billion mark. The lucrative preeminence of biologics is set to continue, bolstered by the introduction of innovative molecular delivery strategies, such as antibody-targeted conjugates, fragments and fusions, as well as by the robust staying power of market leaders. The latter phenomenon is an inevitable consequence of the higher-than-usual regulatory hurdles faced by conventional generic manufacturers seeking to make biosimilars: intended copies of off-patent biologics that, having undergone a strict comparability exercise, are approved by regulatory agencies such as the EMA and the FDA.

This course will survey this changing landscape within an historical framework and will highlight critical scientific and process parameters unique to biologics, that set them aside from conventional small-molecule medicines, including their molecular architecture and mechanisms of action, manufacturing considerations, analytical and functional lot release assays and clinical trial design. We will explore some of the pitfalls by examining a roster of clinical case studies. The capacity of payers to afford these increasingly high-cost therapies in the face of current economic trends will be discussed.

The broad goals of the course are as follows: a detailed understanding of the complexities associated with biologic drugs; a broad familiarity with biologics manufacturing and its inherent variability; a critical understanding of the aspects of biosimilarity; and a familiarity with the clinical implications emerging from the use of biologics.

This graduate course takes students with a basic background in statistics and equips them to tackle massive data sets in health. The focus will be on advanced statistical tests in machine learning and assemble such tests by accessing and validating publicly available code in the R programming language and creating their own code as needed. Students will also learn additional techniques pertaining web scraping, working with unstructured data, data cleaning and data governance building upon the course Data Science in Health I. The course will emphasise creative approaches to analyzing data and how to be critical of misleading analysis. Each class will involve both lecture and weekly tutorial assignments. The major project for the course will involve a large health data set that teams will compete to analyze.

In this course, we discuss data analytics and visualizations using Tableau Desktop and Tableau Prep software. We survey methods in data cleaning and prepping through ETL (Extract, Transform, Loading). We examine best practises in developing dashboards and understand how to tell a story with health and biological data. Our overall goal is to provide a foundation for using Tableau in data-driven decision making in the life sciences. Visualization with Tableau will be supplemented with R coding techniques students have learned from previous courses.

Upon completion of this course, students will be able to frame various classes of healthcare problems as analytics problems using Tableau, to appropriately identify data sources and build visualizations from them. Students will get practice in planning and developing interactive dashboards that help answer analytical problems and provide data accessibility to a non-technical audience.

This course will introduce students to the development of a wide range of product categories and topics pertaining to the commercialisation of health­care products. The course will touch upon medical devices, wearable technology, clinical trial design, biopharmaceuticals, digital health, big data in health, medical apps, biomarkers, medical marketing, treatment guidelines, screening tools, diagnostics, and social listening. Understanding clinical trial design and the regulatory pathway through the US FDA is a major focus of the course. There will be an emphasis on digital health regulation. The course will also introduce students to 3D printing and its applications in health care. Students will be required to get familiar with the digital modelling tool Blender. Students will explore segmentation of physicians based on their clinical practices, drawing upon some of their data science training. The major project for the course will focus on the use of social listening for a product to derive insights into different issues based on the expressed interests of an industry partner. Students will be required to develop a strong level of mastery with the social listening tool Brandwatch and combine that with their foundational knowledge in the course, along with their data science training, to derive insights.

This is an advanced course in machine learning that is focused on the application of neural networks in a health context. The course assumes a strong foundation to create machine learning models in the coding language R. Basic foundations of neural networks are reviewed. Students will learn about the limitations and the appropriate use of neural networks by working on health and biological related data sets.

This course gives students the foundation to engage with healthcare-related information disseminated from websites related to digital health. Product information in healthcare not only has to follow some basic concepts of website design and data collection, but must also be regulatory compliant with respect to such organizations as the Pharmaceutical Advertising Advisory Board (PAB). Students will explore key elements of website design for this purpose. Lastly, students will be exposed to social listening in health as a data gathering tool from web traffic.

This new elective course looks at modern developments in neuroscience and cognitive psychology, that point to new uses of technology to enhance brain function. The course builds its foundation with a neuroanatomy primer, as well as an introduction to the cognitive neuroscience of daydreaming. How can technology be used to aid attention to avoid critical errors? How can better sleep and acts of creativity be supported from emerging technologies? In what way can video games be an aid and a burden to brain function? The major project for the course will explore digital biomarkers for cognitive performance.