Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning

Yousefi, Siamak; Kiwaki, Taichi; Zheng, Yangong; Sugiura, Hiroki; Asaoka, Ryo; Murata, Hiroshi; Lemij, Hans G; Yamanishi, Kenji

doi:10.1016/j.ajo.2018.06.007

Cited by 100 publications

(67 citation statements)

References 21 publications

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“…To date, many groups have applied machine learning and deep learning methods in VF interpretation to detect glaucoma and predict glaucoma progression [7][8][9][10][11][12] . Most algorithms, however, are trained using single glaucoma parameter such as MD or PDP.…”

Section: Introductionmentioning

confidence: 99%

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Song

Chen

et al. 2020

npj Digit. Med.

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By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.

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Section: Introductionmentioning

confidence: 99%

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Song

Chen

et al. 2020

npj Digit. Med.

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“… 42 , 43 In the present study, as we approximately knew the anticipated number of resultant classes for VF test location assignment, 18 , 44 we applied the ISODATA algorithm. As a further point of contrast with the work described by previous authors, which identified clusters of VFs, such as patterns of VF loss, 14 – 17 application of clustering in the present study was to identify clusters of VF spatial test locations that is facilitated by the use of satellite imaging algorithms. Finally, the statistical rigidity of the classification is tested using the D T statistic.…”

Section: Methodsmentioning

confidence: 71%

“…Other studies have used cluster analysis to identify patterns of VF defects appearing in patients with glaucoma both cross-sectionally 12 , 14 , 41 and to detect progression. 15 – 17 In contrast to studies using mixture of Gaussian models, 15 – 17 k means presents a stricter class assignment for a particular datum point with the assumption that the point is highly certain to belong the assigned class, while a mixture of Gaussian incorporates a degree of uncertainty into class assignment. Their utility has been debated in the literature.…”

Section: Methodsmentioning

confidence: 99%

“… 12 , 13 Specifically, others have used clustering methods to characterize the location of VF defects in patients with glaucoma, and to apply these clustered maps to then diagnose or detect progression in others. 14 – 17 Notably, these approaches have used the VF results of patients with pre-existing disease to generate clustered maps.…”

Section: Introductionmentioning

confidence: 99%

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Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma

Phu

Khuu

Bui

et al. 2018

Trans. Vis. Sci. Tech.

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PurposeTo assess the diagnostic utility of a new hemifield asymmetry analysis derived using pattern recognition contrast sensitivity isocontours (CSIs) within the Humphrey Field Analyzer (HFA) 24-2 visual field (VF) test grid. The performance of an optimal CSI-derived map was compared against a commercially available clustering method (Glaucoma Hemifield Test, GHT).MethodsFive hundred VF results of 116 healthy subjects were used to determine normative distribution limits for comparisons. Pattern recognition analysis was applied to HFA 24-2 sensitivity data to determine CSI theme maps delineating clusters for hemifield comparisons. Then, 1019 VF results from 228 glaucoma patients were assessed using different clustering methods to determine the true-positive rate. We also assessed additional 354 VF results of 145 healthy subjects to determine the false-positive rate.ResultsThe optimum clustering method was the CSI-derived seven-theme class map, which identified more glaucomatous VFs compared with the GHT map. The seven-class theme map also identified more cases compared with the five-, six-, and eight-class maps, suggesting no effect of number of clusters. Integrating information regarding the location of glaucomatous defects to the CSI clusters did not improve detection rate.ConclusionsA clustering map derived using CSIs improved detection of glaucomatous VFs compared with the currently available GHT. An optimized CSI-derived map may serve as an additional means to aid earlier detection of glaucoma.Translational RelevancePattern recognition–derived theme maps provide a means for guiding test point selection for asymmetry analysis in glaucoma assessment.

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“…Previous studies have used machine learning approaches to assess glaucomatous damage from visual fields 13,14,30,31 . In these studies, the glaucomatous status of the eyes was determined either by clinical measures or by clinical expertise, making the machine learning performance dependent on the chosen gold standard.…”

Section: Discussionmentioning

confidence: 99%

Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

Berchuck

Mukherjee

Medeiros

2019

Preprint

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Each of the coauthors has seen and agrees with each of the changes made to this manuscript in the revision and to the way his or her name is listed. ABSTRACTPurpose: To develop a novel deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma.Design: Prospective observational cohort. Methods:A variational auto-encoder (VAE) was trained to learn a low-dimensional feature representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. From the VAE, rates were calculated using the average of slopes across latent features from ordinary least squares (OLS) regression and trajectories of the features were used to generate predictions. Results:The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (19% vs. 6%) and four (40% vs 14%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4 th , 6 th and 8 th visits from the first three (e.g., visit eight: VAE8: 4.06 dB vs. PW: 6.06 dB; P<0.001). Conclusion:A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage in the disease.

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Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning

Cited by 100 publications

References 21 publications

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma

Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

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