Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis
Elena Solli, Hiten Doshi, Tobias Elze, Louis Pasquale, Michael Wall, Mark Kupersmith, Elena Solli, Hiten Doshi, Tobias Elze, Louis Pasquale, Michael Wall, Mark Kupersmith
Abstract
Purpose: Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects.
Methods: We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (≤7%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight ≥50%), we compared the ONTT VF classification with the AT pattern.
Results: AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 ± 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36%; 95% confidence interval, 33%-40%), and showed the strongest correlation with MD (r = -0.91; P < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r = -0.77; P < 0.001). Of 191 baseline VFs with a dominant AT, 81% matched the descriptive classifications.
Conclusions: AA identifies and quantifies archetypal, ON-specific patterns of VF loss.
Translational relevance: AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.
Trial registration: ClinicalTrials.gov NCT00000146.
Conflict of interest statement
Disclosure: H. Doshi, None; E. Solli, None; T. Elze, None; L.R. Pasquale, None; M. Wall, None; M. Kupersmith, None
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References
- Beck RW, Cleary PA, Anderson MM Jr., et al. .. A randomized, controlled trial of corticosteroids in the treatment of acute optic neuritis. The Optic Neuritis Study Group. N Engl J Med. 1992; 326(9): 581–8, doi:10.1056/NEJM199202273260901.
- Trobe JD, Beck RW, Moke PS, Cleary PA.. Contrast sensitivity and other vision tests in the Optic Neuritis Treatment Trial. Am J Ophthalmol. 1996; 121(5): 547–53, doi:10.1016/s0002-9394(14)75429-7.
- Beck RW, Cleary PA.. Recovery from severe visual loss in optic neuritis. Arch Ophthalmol. 1993; 111(3): 300, doi:10.1001/archopht.1993.01090030018009.
- Beck RW, Cleary PA, Backlund JC.. The course of visual recovery after optic neuritis. Experience of the Optic Neuritis Treatment Trial. Ophthalmology. 1994; 101(11): 1771–8, doi:10.1016/s0161-6420(94)31103-1.
- Keltner JL, Johnson CA, Cello KE, et al. .. Visual field profile of optic neuritis: a final follow-up report from the Optic Neuritis Treatment Trial from baseline through 15 years. Arch Ophthalmol Chic. 2010; 128(3): 330–337, doi: 10.1001/archophthalmol.2010.16.
- Keltner JL, Johnson CA, Spurr JO, Beck RW.. Baseline visual field profile of optic neuritis. The experience of the Optic Neuritis Treatment Trial. Optic Neuritis Study Group. Arch Ophthalmol. 1993; 111(2): 231–234, doi:10.1001/archopht.1993.01090020085029.
- Eugster MJA, Leisch F.. From Spider-Man to hero - archetypal analysis in R. J Stat Softw. 2009; 30(8): 1–23.
- Cutler A, Breiman L. Archetypal analysis. Technometrics. 1994; 36: 338–47.
- Cai S, Elze T, Bex PJ, Wiggs JL, Pasquale LR, Shen LQ.. Clinical correlates of computationally derived visual field defect archetypes in patients from a glaucoma clinic. Curr Eye Res. 2017; 42(4): 568–74, doi:10.1080/02713683.2016.1205630.
- Elze T, Pasquale LR, Shen LQ, Chen TC, Wiggs JL, Bex PJ.. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface. 2015; 12(103): 20141118, doi:10.1098/rsif.2014.1118.
- Wang M, Pasquale LR, Shen LQ, et al. .. Reversal of glaucoma hemifield test results and visual field features in glaucoma. Ophthalmology. 2018; 125(3): 352–60, doi:10.1016/j.ophtha.2017.09.021.
- Wang M, Shen LQ, Pasquale LR, et al. .. Artificial intelligence classification of central visual field patterns in glaucoma. Ophthalmology. 2020; 127(6): 731–8, doi:10.1016/j.ophtha.2019.12.004.
- Wang M, Tichelaar J, Pasquale LR, et al. .. Characterization of central visual field loss in end-stage glaucoma by unsupervised artificial intelligence. JAMA Ophthalmol. 2020; 138(2): 190–8, doi:10.1001/jamaophthalmol.2019.5413.
- Wang MY, Shen LQ, Pasquale LR, et al. .. An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis. Invest Ophth Vis Sci. 2019; 60(1): 365–75, doi:10.1167/iovs.18-25568.
- Artes PH, Nicolela MT, LeBlanc RP, Chauhan BC.. Visual field progression in glaucoma: total versus pattern deviation analyses. Invest Ophthalmol Vis Sci. 2005; 46(12): 4600–4606, doi:10.1167/iovs.05-0827.
- Saeedi OJ, Elze T, D'Acunto L, et al. .. Agreement and predictors of discordance of 6 visual field progression algorithms. Ophthalmology. 2019; 126(6): 822–8, doi:10.1016/j.ophtha.2019.01.029.
- Greve EL, Heijl A. Seventh International Visual Field Symposium, Amsterdam, September 1986. Documenta ophthalmologica Proceedings series. Hingham, MA: Kluwer Academic; 1987:xvii, 675 p.
- Wall MJC. Morphology and repeatability of automated perimetry using stimulus sizes III, V and VI. Med Res Arch. 2020; 8(6)
- Keltner JL, Johnson CA, Cello KE, et al. .. Classification of visual field abnormalities in the Ocular Hypertension Treatment Study. Arch Ophthalmol Chic. 2003; 121(5): 643–50, doi:10.1001/archopht.121.5.643.
- Doshi H, Solli E, Elze T, Pasquale L, Wall M, Kupersmith M. Unsupervised machine learning identifies quantifiable patterns of visual field loss in idiopathic intracranial hypertension. Transl Vis Sci Technol. 2021; 10(9): 37.
- Anderson DR, Patella VM. Automated static perimetry. 2nd ed. St Louis: Mosby; 1999:xiv, 363 p.
Source: PubMed