Chris A Johnson
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Explore the profile of Chris A Johnson including associated specialties, affiliations and a list of published articles.
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143
Citations
5243
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Recent Articles
1.
Gordon M, Heuer D, Higginbotham E, Parrish R, Liu L, Brandt J, et al.
Am J Ophthalmol
. 2024 Dec;
271:360-370.
PMID: 39647569
Purpose: To determine the rate of visual field (VF) loss before and after the diagnosis of primary open angle glaucoma (POAG) in the Ocular Hypertension Treatment Study (OHTS). Design: Prespecified...
2.
Najdawi W, Jiang F, Zamba G, Johnson C, Pouw A
Ophthalmol Glaucoma
. 2024 Nov;
PMID: 39603460
Purpose: Perimetry is a critical tool for the diagnosis and monitoring of glaucomatous visual field defects. The Humphrey Field Analyzer (HFA) is a large, relatively expensive device that does not...
3.
Huang X, Poursoroush A, Sun J, Boland M, Johnson C, Yousefi S
J Glaucoma
. 2024 Aug;
33(11):815-822.
PMID: 39092996
Prcis: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends...
4.
Sabharwal J, Hou K, Herbert P, Bradley C, Johnson C, Wall M, et al.
Sci Rep
. 2023 Jan;
13(1):1041.
PMID: 36658309
Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently...
5.
Kruger J, Almer Z, Almog Y, Aloni E, Bachar-Zipori A, Bialer O, et al.
J Neuroophthalmol
. 2022 Oct;
42(4):483-488.
PMID: 36255113
Background: A multitude of terms have been used to describe automated visual field abnormalities. To date, there is no universally accepted system of definitions or guidelines. Variability among clinicians creates...
6.
Huang X, Sun J, Gupta K, Montesano G, Crabb D, Garway-Heath D, et al.
Front Med (Lausanne)
. 2022 Oct;
9:923096.
PMID: 36250081
Objective: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields. Design: Algorithm development for discriminating normal...
7.
Yousefi S, Pasquale L, Boland M, Johnson C
Ophthalmology
. 2022 Jul;
129(12):1402-1411.
PMID: 35817199
Purpose: To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. Design: Cross-sectional and longitudinal study. Participants:...
8.
Huang X, Saki F, Wang M, Elze T, Boland M, Pasquale L, et al.
J Glaucoma
. 2022 Jun;
31(8):626-633.
PMID: 35658070
Objective: The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs). Subjects And Participants: A total of 13,231 VFs from...
9.
10.
Harris P, Johnson C, Chen Y, Fann H, Gafford G, Kim Y, et al.
Optom Vis Sci
. 2022 Apr;
99(4):372-382.
PMID: 35383736
Significance: Both the Melbourne Rapid Fields (MRF) tablet and home versions are easy-to-use, portable, and low-cost and accurate methods of evaluating visual fields. Purpose: This study aimed to investigate the...