Judith Massmann

Research Title: The ORBIT CADASIL Study: OculaR Biomarkers from spatial Image analysis Techniques for CADASIL

Supervisors: Dr Lisa Nivison-Smith, Dr Danit Saks, Dr Jessie Huang-Lung, Prof Dr Arcot Sowmya

Abstract

CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy) is the most common monogenic form of cerebral small vessel disease (cSVD). However, its effects on the retina remain largely unknown. The retina shares developmental and microvascular characteristics with the brain. Therefore, retinal changes may serve as a non-invasive window into the brain and the disease processes. The ORBIT CADASIL study uses the ongoing AusCADASIL cohort to identify and validate retinal biomarkers of CADASIL through automated, quantitative analysis of multiple imaging modalities. By combining classical image analysis with machine learning and refining models through explainable AI and clinician-guided training, this project bridges the gap between computational methods and clinical expertise. A multimodal approach further explores correlations across retinal, neuropsychological and neuroimaging data, contributing new insights into CADASIL's systemic disease profile.

Biography

Judith Massmann is a PhD candidate at the School of Optometry and Vision Science, UNSW Sydney, with a background in biophysics, physics and machine learning. Her current research focuses on developing automated methods to analyse retinal images and investigate ocular biomarkers in CADASIL, an inherited cerebral small vessel disease, with the aim of supporting non-invasive disease assessment and monitoring.

Her previous research spans computational biology, neuroscience and vision science. During her bachelor’s thesis, she developed a three-dimensional agent-based model to investigate how cell adhesion and diffusion influence cell sorting in early mammalian development. She subsequently developed and tested machine learning models to improve understanding of autism spectrum disorder. Her published work also includes applying deep learning to smartphone-based vision screening in children. Across these areas, she is interested in using computational methods to understand biological processes and develop accessible tools for healthcare.

Education

• B. Sc. Biophysics
• M. Sc. Physics

Awards

• Awarded University International Postgraduate Award (UIPA) in 2025 for the PhD Study
• Deutschlandstipendium for the Masters

Conference Attendance

• Bernstein Conference 2024, DICTA 2026

Memberships and Affiliations

• Centre for Healthy Brain Ageing
• University of New South Wales