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In modern medicine, a vast amount of digital imaging information is routinely generated in all aspects of patient care. For Dr Ashnil Kumar, this represents an opportunity to view medicine from a different perspective - as a big data problem. His research seeks to develop and apply computing-based solutions that will enable us to look at collections of medical imaging information and find patterns that might indicate particular diagnoses or suggest particular treatments.
"My research involves looking at how computer algorithms can be used to analyse and understand medical imaging data; these algorithms can then be used to provide decision-making support to doctors. Specifically, I aim to derive algorithms that can see things that the human eye cannot easily notice, or algorithms that can discover patterns that may be too complex for humans to easily understand.
"Ultimately, the benefits of my research in enhancing the data-driven dimensions of medicine will flow back to the other dimensions - the biology, the chemistry and, most importantly, the people (both patients and clinicians).
"The ultimate application of my research will be the development of computerised decision-support systems that use image analysis algorithms to provide additional information to doctors, such as highlighting suspicious areas or comparing current images to previous scans to track progress of disease or effectiveness of treatment. This will increase the efficiency and effectiveness of patient care.
"I've been working in this field - and at the University of Sydney - since 2007. Being here means being surrounded by and collaborating with some of the best researchers in Australia and the world, both within and outside my faculty. Being able to also work with staff from the University's clinical schools at RPA, Nepean and Westmead ensures that my research remains clinically relevant."
In modern medicine, a vast amount of digital imaging information is routinely generated in all aspects of patient care. For Dr Ashnil Kumar, this represents an opportunity to view medicine from a different perspective - as a big data problem. His research seeks to develop and apply computing-based solutions that will enable us to look at collections of medical imaging information and find patterns that might indicate particular diagnoses or suggest particular treatments.
"My research involves looking at how computer algorithms can be used to analyse and understand medical imaging data; these algorithms can then be used to provide decision-making support to doctors. Specifically, I aim to derive algorithms that can see things that the human eye cannot easily notice, or algorithms that can discover patterns that may be too complex for humans to easily understand.
"Ultimately, the benefits of my research in enhancing the data-driven dimensions of medicine will flow back to the other dimensions - the biology, the chemistry and, most importantly, the people (both patients and clinicians).
"The ultimate application of my research will be the development of computerised decision-support systems that use image analysis algorithms to provide additional information to doctors, such as highlighting suspicious areas or comparing current images to previous scans to track progress of disease or effectiveness of treatment. This will increase the efficiency and effectiveness of patient care.
"I've been working in this field - and at the University of Sydney - since 2007. Being here means being surrounded by and collaborating with some of the best researchers in Australia and the world, both within and outside my faculty. Being able to also work with staff from the University's clinical schools at RPA, Nepean and Westmead ensures that my research remains clinically relevant."
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Clinical Otolaryngologyno. 6 (2023): 888-894
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Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery (2023)
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Clinical Otolaryngologyno. 3 (2022): 401-413
arxiv(2021)
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