Background: Fundus autofluorescence (FAF) has been described as a topographical map of fluorophores that accumulate within the retinal pigment epithelium as a result of disease. Study aims: To evaluate whether FAF offers information relevant to age-related macular degeneration over that gathered via colour fundus photography (CFP) and optical coherence tomography (OCT). Methods: Ninety-three patients were imaged via CFP, OCT and FAF and the results analysed using Orange Data Mining artificial intelligence and SPSS software. Results: Pupillary dilation makes a significant improvement to FAF image quality. Nuclear sclerotic cataract of > 1.5 on the World Health Organisation scale indicates that there is ≃85% probability that the FAF image will not be of high quality. At > 1.9 there is ≃50% probability of the image not being clinically useful as defined by a novel grading scale. Age was negatively associated with FAF comfort. There is ≥ 90% probability of an abnormal FAF result for an eye with any of the following: > 50 small, > 40 intermediate, > 20 large drusen. Age > 92 years. > 30 packet years of smoking. Any pigmentary abnormalities. ≃80% for any reticular pseudodrusen (RPD). FAF results can be predicted via CFP and OCT data using machine learning with informedness of up to 70.2% and area under the curve (AUC) of 0.903. For transfer learning to be useful within primary care, image pre-processing is likely to be required. Geographic atrophy and pigment epithelial detachments appear to be linked to a patchy FAF pattern. RPD are linked to a reticular FAF pattern. Principle component analysis indicates that drusen were responsible for the greatest percentage of variability in this study’s data (38.6%). Conclusions: Clinical impact: FAF results can be predicted from CFP/OCT via machine learning with 70.2% informedness and AUC of 0.903. Drusen number/size were the most informative variables
