Developing ML solutions for eventual clinical deployment requires a different approach than experimental work done in the context of advancing state-of-the-art (SOTA) models. Global Health Labs, Inc. (GH Labs) is honored to host a MICCAI tutorial focused on key elements for researchers to consider during the initial research phase so that their ML solutions can survive the "valley of death" between the research lab and clinic.
October 12, 2023
Afternoon (half day)
MICCAI, Vancouver
Please visit the MICCAI Conference website to register and obtain streaming instructions for virtual attendance
Developing ML solutions for eventual clinical deployment requires a different approach than experimental work done in the context of advancing state-of-the-art (SOTA) models. Most published ML methods perish in the "valley of death" between the research lab and clinic, often because crucial clinical issues and constraints are underappreciated and thus ignored during initial research. By actively addressing a few key themes during the initial research phase, ML researchers can set up their models for successful deployment.
This tutorial will discuss how these key themes can be handled to prepare for successful deployment to the clinic. This includes design choices for exploratory ML projects, so that even if a project does not seek or reach clinical viability its findings can effectively guide future work, and thus truly contribute to a clinical deployment that benefits patients.
Concrete examples will be drawn from image-based ML projects in global health, including malaria diagnosis, cervical cancer prevention, and lung ultrasound. The tutorial will also provide a window on global health challenges and how ML can actively mitigate global health care inequalities.
The tutorial themes include:
Participants will gain insights about key themes central to any ML project aiming for clinical translation and will acquire concrete tools to optimize for deploying in clinics. Participants will also gain insights into the global health landscape and how ML can benefit currently underserved populations.
This half-day tutorial will include several talks, and ample time for Q & A as well as networking. Some topics are given below.
Welcome and introduction of themes
Keynote
Understanding the use case—why and how to connect with domain expertise
Key issues in data and annotation
Break
RISE (recruiting LMIC researchers)
Use case-focused metrics and model design
Cervical cancer screening with ML—a case study
Closing remarks
To move beyond the sandbox, a proposed ML solution must meet the concrete needs of patients, practitioners, and the business landscape.
Photo © Denis Ngai. Pexels 2020.
A proposed method that violates field realities and constraints (e.g., available gear or currently in-use protocols), or which does not directly address clinical performance needs, is almost certain to fail however fine it may look in theory.
It is crucial to seek out advice from experts who know the details of the medical use-case and care context. Domain experts are central to all the themes listed above.
A case study highlighting pitfalls and challenges at all research phases, when developing ML for deployment.
Photo © Bill & Melinda Gates Foundation/Frederic Courbet
Some regulatory requirements need to be designed into the early stages of algorithm development, to prevent dead ends. For example, FDA requirements may limit the choice of ML algorithms.
Coming soon
Noni Gachuhi has over 21 years of experience in public health program delivery in HIV/AIDS prevention, reproductive health/family planning and malaria programming. She has lived and worked in Kenya, Zimbabwe, Uganda, Rwanda, and India, designing, implementing, and monitoring public health programs.
A renowned researcher in computervision and artificial intelligence, Anandan’s career spans over 30 years inacademia and industry in the US and in India. Excepteur sint occaecat cupidatatnon proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Andrea Lara is the director and founder of BiomedLab, Galileo University, Guatemala's first biomedical engineering research laboratory. Her research field is medical image processing and analysis, focused mainly on cardiac CT. She has a Ph.D. from the Graz University of Technology, a master's degree in biomedical engineering from the University of Lübeck, and worked as a research assistant at the Fraunhofer Institute for Biomedical Engineering.
Courosh Mehanian received a physics PhD from Cornell University. He has worked for three decades in artificial intelligence, having held positions at Boston University, MIT Lincoln Laboratory, the University of Oregon and now GH Labs, where his lab applies ML and computer vision to automated medical image understanding.
Charles Delahunt has 10 years of experience applying ML to global health projects including malaria diagnosis, vitamin A testing via eye videos, ultrasound, helminth egg detection, and pregnancy risk assessment. He has also held a postdoc in the University of Washington’s applied math department, focused on ML methods.
Global Health Labs, Inc. (GH Labs) innovates to reduce health disparities, especially in low- and middle-income countries. As a nonprofit corporation fully funded by Gates Ventures (the private office of Bill Gates), we partner with the Bill & Melinda Gates Foundation and other cross-sector leaders to develop technology solutions that address unmet needs in:
Recent innovations from GH Labs resulted in several commercially available rapid COVID-19 antigen tests, an AI-enabled smartphone app to detect cervical cancer, and a portable cold chain device that enables vaccine delivery to remote communities—among many other tools and technologies advanced for the people who need them most.
(+ 1) 425.777 .9687
Global Health Labs
14360 SE Eastgate Way
Bellevue, WA, 98007-6462
