II.B. Myopic Vitreopathy

Gale, Jesse; Ikuno, Yasushi

doi:10.1007/978-1-4939-1086-1_8

Cited by 6 publications

(5 citation statements)

References 136 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, quantification of the vitreous fluidic space might enable us to detect earlier manifestation of aging than that from other ocular tissues. In addition, structure of vitreous gel and fluid is reported to be altered in eyes with diabetic retinopathy, myopia, and intraocular inflammation before patients develops the symptoms 33 , 34 . Then, biometric data of vitreous is a possible indicator for subclinical changes of the diseases.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

Takahashi

Mao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

Takahashi

Mao

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Collagen-hyaluronan interaction during the liquefaction process can produce collagen fibril aggregates to form within the vitreous body that scatter light and cause the clinical phenomenon of floaters. 2 – 4 Pathologies, such as diabetes and myopia, 5 , 6 can increase the rate of vitreous liquefaction and the formation of intravitreal collagen aggregates. Age-related posterior vitreous detachment (PVD) causes acute floaters due to light scattering by the dense collagen matrix of the posterior vitreous cortex and its irregular surface interfering with light transmission to the retina.…”

Section: Introductionmentioning

confidence: 99%

Machine Independence of Ultrasound-Based Quantification of Vitreous Echodensities

Hoerig,

Nguyen,

Mamou

et al. 2023

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose Quantitative ultrasound (QUS) provides objective indices of Vision Degrading Myodesopsia (VDM) that correlate with contrast sensitivity (CS). To date, QUS methods were only tested on a single ultrasound machine. Here, we evaluate whether QUS measurements are machine independent. Methods In this cross-sectional study, 47 eyes (24 subjects; age = 53.2 ± 14.4 years) were evaluated with Freiburg acuity contrast testing (%Weber), and ultrasonography using 2 machines: one with a 15-MHz single-element transducer and one with a 5-ring, 20-MHz annular-array. Images were acquired from each system in sequential scans. Artifact-free, log-compressed envelope data were processed to yield three parameters (mean amplitude, M ; energy, E ; and percentage filled by echodensities, P50 ) and a composite score ( C ). A B-mode normalization method was applied to the 20-MHz datasets to match QUS parameters at both frequencies. Statistical analyses were performed to evaluate correlations among CS, E , M , P50 , and C for both machines. Results QUS parameters from each machine correlated with CS ( R ≥ 0.57, P < 0.001) and there was correlation between machines ( R ≥ 0.84, P < 0.001). Correlations between CS and QUS parameters were statistically similar for both machines ( P ≥ 0.14) except when the 20-MHz data were normalized ( P = 0.04). Reproducibility of QUS parameters computed from 20-MHz data were satisfactory (52.3%–96.3%) with intraclass correlation values exceeding 0.80 ( P < 0.001). Conclusions The high correlation between QUS parameters from both machines combined with a statistically similar correlation to CS suggests QUS is an effective, machine-independent, quantitative measure of vitreous echodensities. Translational Relevance QUS may be applied across clinical ophthalmic ultrasound scanners and imaging frequencies to effectively evaluate VDM.

show abstract

“…With age, they become more and more thick and irregular and are common in young axial myopia. In old age, the glass body liquefies and forms a cavity, and light scattering from the glass cavity wall may cause a floating phenomenon [ 15 , 16 , 17 , 18 , 19 ]. Secondary floaters are exogenous turbidity in the vitreous that is usually composed of protein, amyloids, or cells.…”

Section: Introductionmentioning

confidence: 99%

Design of a Functional Eye Dressing for Treatment of the Vitreous Floater

Fan

Huang

Nguyêñ

et al. 2022

JPM

View full text Add to dashboard Cite

With the rapid development of display technology, related diseases of the human eye are also increasing day by day. Eye floaters are one of the diseases that affect humans. Herein, we present a functional ophthalmic dressing that can permeate the skin tissues of the eyes through oxygen and hydrogen to improve the symptoms of floaters. In clinical tests, the symptoms of sensory floaters improved in 28 patients, and the recovery rates of mild, moderate, and severe floaters were about 70%, 66.7%, and 83.3%, respectively.

show abstract

II.B. Myopic Vitreopathy

Cited by 6 publications

References 136 publications

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

Machine Independence of Ultrasound-Based Quantification of Vitreous Echodensities

Design of a Functional Eye Dressing for Treatment of the Vitreous Floater

Contact Info

Product

Resources

About