Organisation:
Department:
Keywords:
Interests:
I analyse retinal images with relation to cardiovascular and dementia risk factors. In comparison to other forms of imaging (e.g., MRI), retinal imaging is more cost-effective and widely available.
I work across several groups, including PREVENT-Dementia, Mild Stroke Study 1, and UK Biobank.
My current focus is on developing an automatic method for locating and quantifying optic disc pallor in fundus photographs, which may may associated with a loss of retinal ganglion cells.
Key Publications:
Gibbon, Samuel, et al. “A method for quantifying sectoral optic disc pallor in fundus photographs and its association with peripapillary RNFL thickness.” arXiv preprint arXiv:2311.07213 (2023).
Gibbon, Samuel, et al. “Multi-modal retinal imaging for investigating neurovascular health.” Eye (2023): 1-2.
Gibbon, Samuel, et al. “Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.” Brain and Language 220 (2021): 104968.
Software Expertise:
Image processing, MATLAB, R, Python, Linux