Abstract

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 comprehensive overview of the latest developments in this rapidly evolving field. As AI continues to revolutionize healthcare, we believe it is essential to explore its potential and discuss the challenges and opportunities it presents.

The seminar series will feature prominent experts in the field who will share their research and insights on a range of topics, including AI applications in disease diagnosis, drug discovery, and patient care. Each seminar will involve two talks, followed by an interactive discussion with a light lunch! We hope that this seminar series will be a valuable platform for researchers, practitioners and students to learn about the latest trends and explore collaborations in the exciting field of AI in Medicine.

The next seminar will be held on 26 March 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. This month will feature the following two talks:

Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings – Dr Delshad Vaghari, Research Associate at Department of Psychology, University of Cambridge

Delshad Vaghari is a post-doctoral research associate working at the 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 of a broad range of neuroimaging techniques (MEG, MRI , PET, etc) to study dementia. Currently, he is working on developing AI models for the prognosis and diagnosis of dementia and drug development. Delshad 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 in 2022, supervised by Professor Rik Henson (MRC-CBU, U. of Cambridge) and Professor Ehsanollah Kabir (Tarbiat Modares University). His PhD work introduced a novel Machine Learning framework to combine MEG and MRI to classify MCI patients showing that MEG adds beyond structural MRI . As a part of his PhD, Delshad investigated MEG biomarkers for MCI . The project also published the largest available MEG dataset to study dementia – the BioFIND dataset.

Abstract: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic. We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (structural 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 common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore). PPM robustly predicts whether patients at early disease stages (MCI) will remain stable or progress to Alzheimer’s Disease (AD). PPM generalizes from research to real-world patient data across memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e. predictive prognostic index) that predicts progression to AD more precisely than standard clinical markers (grey matter atrophy, cognitive scores) or clinical diagnosis, reducing misdiagnosis. Our results demonstrate a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for translation to clinical settings.

Leveraging real-world histopathology datasets to inform clinical research – Irina Zhang, Data Scientist at AstraZeneca, Cambridge

Irina’s research focuses on applying imaging processing and state-of-the-art ML&AI algorithms in computer vision to analyse digital histopathology images to inform life science research. She is particularly interested in exploring whole slide images from real-world cohorts, the integration of multi-modal data, and explainable AI for biomedical research.

Abstract: Recent advances in Computational Pathology have demonstrated how we can benefit greatly from applying ML&AI to decipher giga-pixel whole-slide histopathology images. However, it is still incredibly difficult to generalise models developed on high-quality datasets to heterogeneous tissue samples collected in clinical settings. We have investigated various real-world evidence cohorts to address the inherent challenges of real-world histopathology images and develop interpretable and generalizable AI pipelines to inform our clinical research, with the perspective to apply advanced digital pathology to clinical settings and benefit patients in various therapeutic areas.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk