Tom Julian
University of Manchester & Manchester Royal Eye Hospital
AKA Thomas H. Julian
University of Manchester
Manchester Royal Eye Hospital
Manchester, UK
I am an ophthalmologist and clinical academic working across image phenotyping, artificial intelligence, genetics, and multi-omics. My research investigates how detailed features derived from ophthalmic images can advance our understanding of eye disease and reveal relationships between the eye and systemic health.
My work spans scientific discovery and clinical translation: developing image phenotypes, investigating their genetic and biological basis, building clinical research datasets, and evaluating whether these findings can support useful clinical tools. I am particularly interested in collaborations that bring together academics, clinicians, and industry partners around important clinical questions.
Research themes
My research connects four areas: image phenotyping, genetic and multi-omic discovery, the eye and systemic health, and the development of research datasets for clinical translation.
Oculomex
Alongside my academic work, I serve as Clinical Lead at Oculomex, where I contribute a clinical perspective to the development and evaluation of AI-enabled approaches to assessing systemic health through ophthalmic imaging.
This role allows me to explore the practical requirements for responsible translation, from identifying clinically meaningful questions to validation and potential integration into care pathways.
Current work
Multimodal clinical datasets
Building large, multimodal ophthalmic research datasets from routinely collected clinical data in Manchester, supported by secured funding and appropriate approvals.
Disease risk modelling
Developing and evaluating oculomics approaches that use ophthalmic images to model cardiovascular, neurological, and other systemic health risks, with an emphasis on robust evaluation and clinical relevance.
Image phenotyping and biology
Combining deep learning-derived image phenotypes with genetic, metabolomic, and other omics datasets to investigate the biological mechanisms underlying retinal structure and disease.
Collaboration
I welcome conversations with academic and industry partners interested in ophthalmic imaging, image phenotyping, genetics, multi-omic research, clinically grounded AI evaluation, prospective studies, and responsible clinical translation.
I am also happy to discuss research supervision, collaborative grant development, and speaking opportunities. Read more about how I collaborate or get in touch.
Latest work
| Jun 16, 2026 | Our multi-omic study of deep learning-derived ophthalmic imaging phenotypes is now published in Nature Cardiovascular Research. The work links imaging features with genetic, metabolic, anatomical, cardiovascular, and neurological traits. Read the paper. |
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| Mar 31, 2026 | We published a review in Current Eye Research examining retinal features reported in people with stroke. Read the paper. |
| Mar 20, 2026 | Our collaborative work with the team at SITraN investigates metabolites with potential causal roles in motor neurone disease risk. Read the paper. |
| Nov 18, 2025 | We published a review examining the potential role of optical coherence tomography in cardiovascular risk prediction. |
Selected publications
- Journal articleMulti-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traitsNature Cardiovascular Research, 2026
- Journal articlePigmentation and retinal pigment epithelium thickness: a study of the phenotypic and genotypic relationships between ocular and extraocular pigmented tissuesPigment Cell & Melanoma Research, 2025
- Journal articlePhysical exercise is a risk factor for amyotrophic lateral sclerosis: Convergent evidence from Mendelian randomisation, transcriptomics and risk genotypesEBioMedicine, 2021