This seminar will be held on Tuesday 26 March, 2:00-13:00 at the Jeffrey Cheah Biomedical Centre Main Lecture Theatre, University of Cambridge and streamed online via zoom.
To sign up on Eventbrite click here.
A light lunch will be provided from 11:50 from Aromi.
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
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
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: Meeting ID: 990 5046 7573 and Passcode: 617729
