Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning

We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF and optical coherence tomography (OCT) macula images from 93 ON eyes and 70 normal fellow eyes ≥ 90 days after acute ON. We correlated archetype (AT) weights (total weight = 100%) of VFs and total retinal thickness (TRT), inner retinal thickness (IRT), and macular ganglion cell-inner plexiform layer (GCIPL) thickness. AA showed most ON eyes had a 10-2 VF pattern like the normal fellow eye VF, despite having markedly thinner GCIPL patterns. AA identified 7 VF and 11 retinal thickness ATs for each OCT model. The normal VF AT constituted 80% of ON eyes and 90% of normal fellow eyes. The most common GCIPL AT consisted of diffuse thinning. We identified significant correlations for the normal AT weights using OCT AT weights of five GCIPL ATs (r = 0.45), four TRT ATs (0.53) and two IRT ATs (0.42). Following acute ON, most eyes had complete 10-2 VF recovery despite significant GCIPL thinning, suggesting compensatory mechanisms for vision.