Literature DB >> 31895454

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.

Mengyu Wang1, Jorryt Tichelaar1,2,3, Louis R Pasquale4,5, Lucy Q Shen6, Michael V Boland7, Sarah R Wellik8, Carlos Gustavo De Moraes9, Jonathan S Myers10, Pradeep Ramulu7, MiYoung Kwon11, Osamah J Saeedi12, Hui Wang1,13, Neda Baniasadi1, Dian Li1, Peter J Bex14, Tobias Elze1,15.   

Abstract

Importance: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss. Objective: To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma. Design, Setting, and Participants: This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019. Main Outcomes and Measures: Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones.
Results: Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not. Conclusions and Relevance: In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss.

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Mesh:

Year:  2020        PMID: 31895454      PMCID: PMC6990977          DOI: 10.1001/jamaophthalmol.2019.5413

Source DB:  PubMed          Journal:  JAMA Ophthalmol        ISSN: 2168-6165            Impact factor:   7.389


  27 in total

1.  Prior rates of visual field loss and lifetime risk of blindness in glaucomatous patients undergoing trabeculectomy.

Authors:  W S Foulsham; L Fu; A J Tatham
Journal:  Eye (Lond)       Date:  2015-08-28       Impact factor: 3.775

2.  Prevalence and nature of early glaucomatous defects in the central 10° of the visual field.

Authors:  Ilana Traynis; Carlos G De Moraes; Ali S Raza; Jeffrey M Liebmann; Robert Ritch; Donald C Hood
Journal:  JAMA Ophthalmol       Date:  2014-03       Impact factor: 7.389

3.  Size threshold perimetry performs as well as conventional automated perimetry with stimulus sizes III, V, and VI for glaucomatous loss.

Authors:  Michael Wall; Carrie K Doyle; Trina Eden; K D Zamba; Chris A Johnson
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-06-07       Impact factor: 4.799

4.  False-negative responses in glaucoma perimetry: indicators of patient performance or test reliability?

Authors:  B Bengtsson; A Heijl
Journal:  Invest Ophthalmol Vis Sci       Date:  2000-07       Impact factor: 4.799

5.  Reliability of visual field results over repeated testing.

Authors:  J Katz; A Sommer; K Witt
Journal:  Ophthalmology       Date:  1991-01       Impact factor: 12.079

6.  Patterns of functional vision loss in glaucoma determined with archetypal analysis.

Authors:  Tobias Elze; Louis R Pasquale; Lucy Q Shen; Teresa C Chen; Janey L Wiggs; Peter J Bex
Journal:  J R Soc Interface       Date:  2015-02-06       Impact factor: 4.118

7.  Evaluation of retinal nerve fiber layer progression in glaucoma a prospective analysis with neuroretinal rim and visual field progression.

Authors:  Christopher Kai Shun Leung; Shu Liu; Robert N Weinreb; Gilda Lai; Cong Ye; Carol Yim Lui Cheung; Chi Pui Pang; Kwok Kay Tse; Dennis Shun Chiu Lam
Journal:  Ophthalmology       Date:  2011-04-29       Impact factor: 12.079

8.  Classification of visual field abnormalities in the ocular hypertension treatment study.

Authors:  John L Keltner; Chris A Johnson; Kimberly E Cello; Mary A Edwards; Shannan E Bandermann; Michael A Kass; Mae O Gordon
Journal:  Arch Ophthalmol       Date:  2003-05

9.  The Impact of Location of Progressive Visual Field Loss on Longitudinal Changes in Quality of Life of Patients with Glaucoma.

Authors:  Ricardo Y Abe; Alberto Diniz-Filho; Vital P Costa; Carolina P B Gracitelli; Saif Baig; Felipe A Medeiros
Journal:  Ophthalmology       Date:  2015-12-15       Impact factor: 12.079

10.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.

Authors: 
Journal:  JAMA       Date:  2013-11-27       Impact factor: 56.272

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  7 in total

1.  Artificial intelligence improves accuracy, efficiency, and reliability of a handheld infrared eccentric autorefractor for adult refractometry.

Authors:  Yi-Ting Cao; Dan-Yang Che; Yi-Lei Pan; Yun-Li Lu; Chong-Yang Wang; Xiao-Li Zhang; Yun-Fei Yang; Ke-Ke Zhao; Ji-Bo Zhou
Journal:  Int J Ophthalmol       Date:  2022-04-18       Impact factor: 1.779

2.  Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic.

Authors:  Brian C Stagg; Joshua D Stein; Felipe A Medeiros; Barbara Wirostko; Alan Crandall; M Elizabeth Hartnett; Mollie Cummins; Alan Morris; Rachel Hess; Kensaku Kawamoto
Journal:  Ophthalmol Glaucoma       Date:  2020-08-15

3.  Foveal crowding appears to be robust to normal aging and glaucoma unlike parafoveal and peripheral crowding.

Authors:  Foroogh Shamsi; Rong Liu; MiYoung Kwon
Journal:  J Vis       Date:  2022-07-11       Impact factor: 2.004

Review 4.  A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.

Authors:  Atalie C Thompson; Alessandro A Jammal; Felipe A Medeiros
Journal:  Transl Vis Sci Technol       Date:  2020-07-22       Impact factor: 3.283

5.  Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.

Authors:  Elena Solli; Hiten Doshi; Tobias Elze; Louis Pasquale; Michael Wall; Mark Kupersmith
Journal:  Transl Vis Sci Technol       Date:  2022-01-03       Impact factor: 3.048

6.  Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model.

Authors:  Wendy W Liu; Michael McClurkin; Edem Tsikata; Pui-Chuen Hui; Tobias Elze; Ali R C Celebi; Ziad Khoueir; Ramon Lee; Eric Shieh; Huseyin Simavli; Christian Que; Rong Guo; Johannes de Boer; Teresa C Chen
Journal:  J Glaucoma       Date:  2020-10       Impact factor: 2.290

7.  Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.

Authors:  Bettina Teng; Dian Li; Eun Young Choi; Lucy Q Shen; Louis R Pasquale; Michael V Boland; Pradeep Ramulu; Sarah R Wellik; Carlos Gustavo De Moraes; Jonathan S Myers; Siamak Yousefi; Thao Nguyen; Yuying Fan; Hui Wang; Peter J Bex; Tobias Elze; Mengyu Wang
Journal:  Transl Vis Sci Technol       Date:  2020-11-17       Impact factor: 3.048

  7 in total

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