New visual field indices of disharmony for early diagnosis of glaucoma, alone or associated with conventional parameters

Abreu-González, R.; González-Hernández, Marta; Pena-Betancor, Cristina; Rodriguez-Esteve, Paloma; Rosa, Manuel González de la

doi:10.1177/1120672118762668

Cited by 2 publications

(2 citation statements)

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“…The threshold coefficient of variation (TCV) is an index of regularity described by our research group, which represents the harmony of one’s own visual field, without comparing it with patterns of normality [ 18 ]. A characteristic for normality is stability or harmony, and for pathology, is irregularity or fluctuation.…”

Section: Methodsmentioning

confidence: 99%

“…The aim of this paper was to take advantage of these new automatic procedures based on experience to facilitate the use of the application and to improve its reproducibility, sensitivity, and specificity. In particular, an attempt was made to combine the morphological and perfusion information provided by the method with functional perimetric data representative of visual field homogeneity [ 18 ] to assess whether this provided robustness in the diagnostic decision.…”

Section: Introductionmentioning

confidence: 99%

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Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma

González-Hernández

Gonzalez-Hernandez

Perez-Barbudo

et al. 2021

JCM

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Background: Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated. Methods: The morphology and perfusion estimated by Laguna ONhE were compiled into a “Globin Distribution Function” (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters. Results: The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained with the combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity). Conclusion: Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma

González-Hernández

Gonzalez-Hernandez

Perez-Barbudo

et al. 2021

JCM

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Segmentation of the Optic Nerve Head Based on Deep Learning to Determine its Hemoglobin Content in Normal and Glaucomatous Subjects

Gonzalez-Hernandez¹,

Díaz-Alemán²,

Perez-Barbudo³

et al. 2018

J Clin Exp Ophthalmol

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New visual field indices of disharmony for early diagnosis of glaucoma, alone or associated with conventional parameters

Abstract: Disharmony combined with normative value-based indices and/or optical coherence tomography indices is useful for very specific early diagnosis of glaucoma.

Cited by 2 publications

References 25 publications

Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma

Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma

Segmentation of the Optic Nerve Head Based on Deep Learning to Determine its Hemoglobin Content in Normal and Glaucomatous Subjects

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