Catarina P. Coutinho

The purpose of this study was the identification of the characteristic visual field loss patterns of dominant optic atrophy (DOA) employing the Archetypal Analysis (AA) machine learning algorithm in Python 3.8. For 64 patients affected by molecularly confirmed DOA with OPA1 heterozygous mutation, binocular visual field (VF) tests performed by SITA standard 30-2 Humphrey VF analyser (Carl Zeiss Meditec, Dublin, CA, USA) were collected. When available, multiple VF tests from different time points were collected to enlarge the dataset. Considering 229 VF test, preliminary results using AA detected archetypes (AT) for the characterisation of visual loss in DOA. Central ATs revealed to be the most significant ones, matching the common characteristic central or ceco-central scotoma for DOA patients, followed by the quadrantanopia, nasal step, and altitudinal ATs. The primary implementation of this algorithm focused on the DOA disease evidenced the potential to help in the identification and distinction of the typical visual loss patterns, which is in line with other works focused on diseases as glaucoma and idiopathic intracranial hypertension.