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SUMMARY:Cambridge MedAI Seminar Series - Dr Delshad Vaghari (University of
  Cambridge) and Irina Zhang (Astrazeneca)
DTSTART:20240326T120000Z
DTEND:20240326T130000Z
UID:TALK212905@talks.cam.ac.uk
CONTACT:Ines Machado
DESCRIPTION:The *Cancer Research UK Cambridge Centre* and the *Department 
 of Radiology at Addenbrooke's* are pleased to announce a seminar series on
  *Artificial Intelligence (AI) in Medicine*\, which aims to provide a comp
 rehensive overview of the latest developments in this rapidly evolving fie
 ld. As AI continues to revolutionize healthcare\, we believe it is essenti
 al to explore its potential and discuss the challenges and opportunities i
 t presents.\n\nThe seminar series will feature prominent experts in the fi
 eld who will share their research and insights on a range of topics\, incl
 uding AI applications in disease diagnosis\, drug discovery\, and patient 
 care. *Each seminar will involve two talks\, followed by an interactive di
 scussion with a light lunch!* We hope that this seminar series will be a v
 aluable platform for researchers\, practitioners and students to learn abo
 ut the latest trends and explore collaborations in the exciting field of A
 I in Medicine.\n\nThe next seminar will be held on *26 March 2024\, 12-1pm
  at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universit
 y of Cambridge and streamed online via Zoom.* This month will feature the 
 following two talks:\n\n*Robust and interpretable AI-guided marker for ear
 ly dementia prediction in real-world clinical settings - Dr Delshad Vaghar
 i\, Research Associate at Department of Psychology\, University of Cambrid
 ge*\n\nDelshad Vaghari is a post-doctoral research associate working at th
 e Adaptive Brain Lab\, University of Cambridge. His research uses machine/
 deep learning to study neurodegenerative diseases. His main interests are 
 the interface of AI and brain sciences. He is also interested in the use o
 f a broad range of neuroimaging techniques (MEG\, MRI\, PET\, etc) to stud
 y dementia. Currently\, he is working on developing AI models for the prog
 nosis and diagnosis of dementia and drug development.\nDelshad completed a
  BSc in BioMedical Engineering followed by an MSc in Signal Processing in 
 Iran. He was awarded his PhD in Machine Learning and Pattern Recognition i
 n 2022\, supervised by Professor Rik Henson (MRC-CBU\, U. of Cambridge) an
 d Professor Ehsanollah Kabir (Tarbiat Modares University). His PhD work in
 troduced a novel Machine Learning framework to combine MEG and MRI to clas
 sify MCI patients showing that MEG adds beyond structural MRI. As a part o
 f his PhD\, Delshad investigated MEG biomarkers for MCI. The project also 
 published the largest available MEG dataset to study dementia - the BioFIN
 D dataset.\n\n\nAbstract: Predicting dementia early has major implications
  for clinical management and patient outcomes. Yet\, we still lack sensiti
 ve tools for stratifying patients early\, resulting in patients being undi
 agnosed or wrongly diagnosed. Despite rapid expansion in machine learning 
 models for dementia prediction\, limited model interpretability and genera
 lizability impede translation to the clinic. We build a robust and interpr
 etable predictive prognostic model (PPM) and validate its clinical utility
  using real-world\, routinely-collected\, non-invasive\, and low-cost (str
 uctural MRI scan\, cognitive scales) patient data. To enhance scalability 
 and generalizability to the clinic\, we: 1) train the PPM with clinically-
 relevant predictors (grey matter atrophy\, clinical scales) that are commo
 n across research and clinical cohorts\, 2) test PPM predictions with inde
 pendent multicenter real-world data from memory clinics across countries (
 UK\, Singapore). PPM robustly predicts whether patients at early disease s
 tages (MCI) will remain stable or progress to Alzheimer’s Disease (AD). 
 PPM generalizes from research to real-world patient data across memory cli
 nics and its predictions are validated against longitudinal clinical outco
 mes. PPM allows us to derive an individualized AI-guided multimodal marker
  (i.e. predictive prognostic index) that predicts progression to AD more p
 recisely than standard clinical markers (grey matter atrophy\, cognitive s
 cores) or clinical diagnosis\, reducing misdiagnosis. Our results demonstr
 ate a robust and explainable clinical AI-guided marker for early dementia 
 prediction that is validated against longitudinal\, multicenter patient da
 ta across countries\, and has strong potential for translation to clinical
  settings.\n\n\n*Leveraging real-world histopathology datasets to inform c
 linical research - Irina Zhang\, Data Scientist at AstraZeneca\, Cambridge
 *\n\nIrina’s research focuses on applying imaging processing and state-o
 f-the-art ML&AI algorithms in computer vision to analyse digital histopath
 ology images to inform life science research. She is particularly interest
 ed in exploring whole slide images from real-world cohorts\, the integrati
 on of multi-modal data\, and explainable AI for biomedical research.\n\n\n
 Abstract: Recent advances in Computational Pathology have demonstrated how
  we can benefit greatly from applying ML&AI to decipher giga-pixel whole-s
 lide histopathology images. However\, it is still incredibly difficult to 
 generalise models developed on high-quality datasets to heterogeneous tiss
 ue samples collected in clinical settings. We have investigated various re
 al-world evidence cohorts to address the inherent challenges of real-world
  histopathology images and develop interpretable and generalizable AI pipe
 lines to inform our clinical research\, with the perspective to apply adva
 nced digital pathology to clinical settings and benefit patients in variou
 s therapeutic areas.\n\nThis is a *hybrid event so you can also join via Z
 oom*: \n\nhttps://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz
 09\n\nMeeting ID: 990 5046 7573 and Passcode: 617729\n\n\nWe look forward 
 to your participation! If you are interested in getting involved and prese
 nting your work\, please email *Ines Machado at im549@cam.ac.uk*\n
LOCATION:Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universi
 ty of Cambridge
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