Computer Vision News Computer Vision News 22 and AI for pancreatic cancer detection. It aims to establish how well radiologists currently perform in detecting pancreatic cancer using contrast-enhanced CTs. This benchmarking is crucial as it sets a standard for AI systems to match or exceed. Furthermore, the challenge seeks to develop AI capable of detecting pancreatic cancer, ideally outperforming or at least matching the expertise of radiologists. It is being organized by PANCAIM, a consortium to develop AI to improve pancreatic cancer diagnosis, prognosis, and treatment. Megan was hired to develop multimodal AI. “We were granted Horizon 2020 funding to see if we can make an impact with AI on the very difficult topic of pancreatic cancer,” she recalls. “This is the start to see where AI could be impactful in the future, hopefully, in a sort of opportunistic screening setting in which we can have AI running in the background of contrast-enhanced CTs to see if we can catch these cancers a bit earlier.” AI’s potential in this context is significant. Radiologists will not need to check every contrasting CT because AI can flag any that warrant further investigation, including those not intended to investigate the pancreas. It could be especially beneficial in peripheral hospitals and non-specialist settings, where expertise in identifying subtle early signs of pancreatic cancer may be limited. “The highest level of achievement we could get is, let’s say, we develop this AI and get it running in every hospital that uses CT,” John suggests. “Everybody can use it in the background, and every CT scan will be screened for pancreatic cancer, and there will be some kind of red flag saying this pancreas is not normal and an expert or non-expert should have a look at it. Then you can pick out the cases earlier, which will be the ideal situation.” John’s main research focus is pancreatic imaging. He develops new techniques to understand better how the pancreas functions and what kinds of tumors there are, using advanced imaging techniques to gain insights into the disease. In the last 10 years, he tells us there has been a fundamental shift toward using AI in medical image analysis. Grand Challenge - Medical Imaging
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