By: Cormac Rea
There is an urgent need amongst healthcare researchers for creative solutions to address the challenges of caring for our growing aging population in diverse healthcare settings, including the need to predict disease development and treatment outcomes. Data science, including artificial intelligence (AI), can revolutionize how we understand the human brain, offering more affordable, precise tools for detecting neurodegenerative conditions.
However, AI’s integration into clinical practice faces a major barrier in terms of the multidisciplinary collaboration necessary to design, implement, and refine AI tools effectively. Data scientists working with predictive algorithm development may lack clinical context to tailor these tools for real-world healthcare workflows. At the same time, healthcare leaders must collaborate with both scientists and clinicians to ensure AI-informed decisions are sound and impactful.
Enter Advancing Aging and Neurodegeneration Research through Data Science — a unique initiative by the Data Sciences Institute’s (DSI) Emergent Data Science Program that aims to bridge these gaps by fostering learning and training opportunities between data scientists, basic scientists, clinicians, and educators. The initiative is led by professors Rosanna Olsen (Rotman Research Institute, Baycrest Academy for Research and Education; Department of Psychology, Faculty of Arts & Science, University of Toronto); Malcolm Binns (Rotman Research Institute, Baycrest Academy for Research and Education; Dalla Lana School of Public Health, University of Toronto); Bradley Buchsbaum (Rotman Research Institute, Baycrest Academy for Research and Education; Department of Psychology, Faculty of Arts & Science, University of Toronto); Jean Chen (Rotman Research Institute, Baycrest Academy for Research and Education; Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto) and Kamil Uludag (Krembil Brain Institute, University Health Network; Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto).
The AI & Aging team will provide training and learning opportunities, bringing together data scientists, clinicians, educators, to explore the development of new areas of research, which may ultimately benefit the treatment and healthcare service. Additionally, the initiative will showcase experts in data science and aging, creating a forum to highlight and discuss key emergent issues.
The program will launch this spring with a talk from a renowned researcher in neuroimaging and machine learning. Prof. Christos Davatzikos, Wallace T. Miller Sr. Professor of Radiology at the University of Pennsylvania, and Director of the recently founded AI2D Center for AI and Data Science for Integrated Diagnostics, will speak about Machine learning in neuroimaging: understanding the heterogeneity of brain aging and neurodegeneration, and building personalized imaging biomarker on March 27.
Dr. Davatzikos has been the founding Director of the Center for Biomedical Image Computing and Analytics since 2013, and the director of the AI in Biomedical Imaging Lab (AIBIL). He oversees a diverse research program ranging from basic problems of imaging pattern analysis and machine learning to a variety of clinical studies of aging and Alzheimer’s disease, schizophrenia, brain cancer, and brain development. He is an IEEE fellow, and a fellow of the American Institute for Medical and Biological Engineering.
“I am thrilled to co-lead this exciting new EDSP program, Advancing Aging and Neurodegeneration Research through Data Science, supported by the Data Science Institute at the University of Toronto,” said Olsen. “This initiative brings together experts who use cutting-edge AI and data-driven approaches to tackle some of the most pressing challenges in aging and neurodegenerative disease research.”
“We are especially honored to kick off the series with Dr. Christos Davatzikos, a true leader in AI-driven biomedical imaging, whose work is transforming how we understand and detect different types of brain disorders.”
The DSI spoke with Dr. Davatzikos about his background, research focus, and the potential future uses of machine learning in aging.
Tell us a little bit about yourself. How did you become interested in your area of research (neuroimaging, aging, machine learning)?
CD: I went through education and training in engineering and computer science but was always interested in biomedical applications of technologies, especially in neuroscience. As machine learning methods were in their infancy in the 90s, I thought that they are the tools needed to help us… to see in the data what we can’t otherwise see. For example, to see brain signatures of neuropsychiatric and neurodegenerative diseases that cannot be detected visually and/ or are predictive of clinical outcomes.
Do you have a favorite paper or research finding from your own group or from other researchers that you would like to share with us?
CD: A recent paper in Nature Medicine on Brain aging patterns in a large and diverse cohort of 49,482 individuals is one of my favorites. It helps us understand the heterogeneity of brain aging trajectories, as well as their genetic, clinical and lifestyle correlates.
What are you most excited about for the future of our field? Do you anticipate any breakthroughs in the field of aging research in the next five years?
CD: Among many potentially exciting directions, I am particularly excited about seeing more emphasis on prevention and early detection. Improving our understanding of the role of genetic and lifestyle risk factors, and being able to identify individuals at risk, can inform clinical trials and personal health management.
Machine learning can play a significant role in this direction in many ways, two of them being the following: 1) it helps us develop endophenotypes, in part by looking at complex patterns of biomarkers of all sorts, and hence identifying individuals who not only have a risk factor, but who also seem to be “expressing” respective endophenotypes/patterns that have been linked with that risk factor; 2) it helps us build predictive models of future brain and clinical trajectories.
Another exciting direction is that of using machine learning methods for drug repurposing and development, by learning more about genetic correlates of brain aging and associated neuropathologic processes and identifying drugs that can slow down these processes.
Since the development process for applied machine learning tools requires multidisciplinary input across an array of clinical, measurement and data experts, do you have suggestions for optimizing collaboration and communication across professionals with different immediate goals?
CD: As other similar technical fields, which have become an integral part of medicine and biomedical research (e.g. medical physics and biostatistics), I think that a new generation of biomedical scientists and clinicians will emerge: people who have cross-training and interests in both data science/AI and biomedical domains.
Do you have any thoughts on sustainable AI, in health research and beyond?
CD: AI is a technology that will become an integral part of our daily lives, including medicine and biomedical research, pretty much like other technologies from farming machines and the automobile, the cellphone and the internet. As such, we will have to develop mechanisms that constantly maintain and enhance AI tools. Due to its nature, AI is a technology that continuously adapts and learns from new data and new knowledge: the more we use it, the better it will become.
Emergent Data Sciences Program
Through the Emergent Data Science Program, DSI funds a broad span of activity that can lead to the development of innovative data science methodologies, deep connections with computation and applied disciplines, new training programs, collaboration, knowledge mobilization, and impact beyond the academy. Applications for the 2025 program are now being accepted. LOI Deadline: March 28
Learn more about the application process.