{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Practical AI","title":"Seeing beyond the scan in neuroimaging","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/c2ab9c38\"></iframe>","width":"100%","height":180,"duration":2578,"description":"In this episode, we explore the intersection of AI, machine learning, and healthcare through the lens of neuroimaging and epilepsy diagnosis. Dr. Gavin Winston shares insights from his work using MRI data and machine learning to uncover subtle abnormalities in brain function. We discuss the cultural and ethical barriers to AI adoption in medicine, how predictive data analysis could transform the diagnostic workflow, and what the future holds for medical imaging in a world increasingly shaped by intelligent systems.\nFeaturing:\nGavin Winston – LinkedIn, Website\nChris Benson – Website, GitHub, LinkedIn, X\nDaniel Whitenack – Website, GitHub, X\nLinks:\nDetection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks: A MELD Study\nMachine Learning in Neuroimaging across Disciplines\nAutomated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS\nLiterature review and protocol for a prospective multicentre cohort study on multimodal prediction of seizure recurrence after unprovoked first seizure\nDeep learning in neuroimaging of epilepsy\nNon-parametric combination of multimodal MRI for lesion detection in focal epilepsy\nDetection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data","thumbnail_url":"https://img.transistorcdn.com/Ox7ZlyiQOhdDa4Qy1MnJH5WFoksAetrzb40Jo1pePFs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wMTZi/ZWJmNWIwNDdmYTcw/NGJjMTExZjNjZmYy/M2ZjNS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}