Abstract:
Estimates for the UK suggest that alcohol consumption during pregnancy and the prevalence of fetal alcohol spectrum disorders (FASD) – the most common neurodevelopmental condition – are high. Considering the significant health and social impacts of FASD, there is a public health imperative to prioritise recognition, prevention, interventions, and support.
In this talk, we (i) outline the current state of play regarding FASD knowledge and research in the UK, with a focus on the clinical challenges faced,(ii) highlight the impacts of polysubstance use such as concomitant alcohol and smoking, and (iii) describe the application of machine learning and computer vision to assist clinicians with the recognition of FASD-associated features used for diagnosis.

Bio:
Michael is a PI and group leader in the WRH and Big Data Institute. He leads a project focused on identifying the interactions between the morphology of the face and brain, and neurocognitive impairments that result from alcohol teratogenesis. Michael and his team are currently developing translational approaches to integrate into the clinical workflow, using machine learning, computer vision and 3D morphological analysis to better understand and identify those affected.