December 2022
Amith Kamath wishes to facilitate faster radiotherapy treatment for patients with glioblastoma through AI-supported therapy planning. The CAIM Young Researcher Award winner appreciates the openness of the Bernese community around AI applications in healthcare, also welcoming ideas from people trained in other disciplines to tackle hard problems in medicine. He is currently pursuing his PhD at the Medical Image Analysis research group of the ARTORG Center for Biomedical Engineering Research and looks forward to translating his research into a clinical tool through the broad entrepreneurial support he is receiving in Bern – including the personalized business coaching by be-advanced as part of his CAIM Award win in the category “translation”.
What drives you in your research?
My research is centered around evaluating the quality of radiotherapy delivered to patients with glioblastoma. Given the usually bad prognosis, people already diagnosed with this tumor currently must wait between one and three weeks until they can start treatment, due to the current workflows in radiotherapy planning. We expect that by using AI models to help draw boundaries around organs while simultaneously estimating the radiation dose and toxicity, radiotherapy treatment can be started earlier before the tumor has progressed further. We hope that someday our work can really add quality to people’s lives in this sense.
What does winning a CAIM Young Researcher Award mean to you?
What matters to me most is that many of us were able to share our research and receive constructive feedback and comments in a setting like the CAIM Symposium. Beyond the award, the existence of such a vibrant community is very rewarding.
For the translational focus of the award, I was fortunate to receive prior exposure through the Innosuisse startup toolbox program “Business Concepts” in October this year. The entrepreneurial coaching opportunity I now have with the CAIM Award is the perfect continuation of that. It would be great to get the experts’ opinion on how we can convert our research into a useful tool or product for clinicians!
If there are ten users of what I build that will mean more to me than writing a PhD thesis that no one may read.
How important is it for you to share your research?
Very much! My background is mostly in image processing and computer vision, and I think it’s great how welcoming the scientific community here is to researchers from other academic backgrounds. I don’t have a biomedicine background, and I believe people without medical schooling can make strong contributions to tackling hard problems in the medical space. Ways of thinking that are commonplace in another field could be novel to healthcare challenges and thus lead to innovative solutions.
I like the people I get to work with daily who motivate me by asking all the right questions. I like that my work is very visual: I find it easier to look at a set of images or a video than at a bunch of equations for an “Aha” moment. When some images are hard to interpret, I appreciate that clinicians are quite open to talk to technical folks like us. This readiness to work with each other and speak the same language is quite important in this line of work.
Therefore, I like to share our research with a broader global community. I use Social Media to exchange ideas with other scientists and our research lab Medical Image Analysis has started a “How to” video series for beginners in Deep Learning for medical imaging, summarizing some of the pitfalls and stumbling blocks in a humorous way (https://github.com/ubern-mia/bender). We are also currently preparing for a symposium on interpretability of AI models at CAIM in March ‘23, with the hope to get a lively discussion going on this important topic for safer AI adoption in medicine.