Subfoveal Choroidal Thickness in 1323 Children Aged 11 to 12 Years and Association With Puberty: The Copenhagen Child Cohort 2000 Eye Study

2014 ◽  
Vol 55 (1) ◽  
pp. 550 ◽  
Author(s):  
Xiao Qiang Li ◽  
Pia Jeppesen ◽  
Michael Larsen ◽  
Inger Christine Munch
Keyword(s):  
Choroidal Thickness ◽  
Subfoveal Choroidal Thickness

Subfoveal choroidal thickness and peripapillary retinal nerve fiber layer thickness in young obese males

2020 ◽  
pp. 112067212098289
Author(s):  
Ceylan Uslu Dogan ◽  
Damla Culha
Keyword(s):  
Nerve Fiber ◽  
Retinal Nerve Fiber Layer ◽  
Choroidal Thickness ◽  
Obese Group ◽  
Fiber Layer ◽  
Subfoveal Choroidal Thickness ◽  
Young Males ◽  
Rnfl Thickness ◽  
Patient Groups ◽  
Temporal Quadrant

Objective: Regarding the effect of obesity on subfoveal choroidal thickness (CT) and peripapillary retinal nerve fiber layer (RNFL) thickness, controversial results have been reported in different patient groups. This study aimed to evaluate the effect of obesity on these parameters among young male subjects in comparison with age-matched non-obese healthy males. Methods: This prospective, cross-sectional study included both eyes of 50 obese young males and 50 healthy non-obese young males. The obese and the non-obese groups included subjects with a BMI of ⩾30 and ⩽25 kg/m², respectively. Subfoveal choroidal thickness and RNFL analyses were conducted by spectral domain optical coherence tomography (SD-OCT). Results: Subfoveal choroidal thickness (321.0 ± 46.7 vs 338.4±35.3, p = 0.002) and RNFL thickness at temporal quadrant (73.4 ± 9.9 vs 76.4 ± 9.3, p = 0.008) was significantly lower in the obese group when compared to the non-obese group. The groups did not differ regarding peripapillary RNFL thickness at other quadrants (superior, inferior, or nasal) or regarding mean peripapillary RNFL thickness. Conclusion: Findings of this study demonstrated a negative correlation of obesity with subfoveal choroidal thickness and temporal quadrant peripapillary RNFL thickness. Larger studies on different patient groups with longer-term follow-up are warranted to better elucidate the ophthalmological effects of obesity.

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

scholarly journals Choroidal thickness and retinal nerve fiber layer analysis in acute systemic brucellosis

2021 ◽  
Vol 19 ◽  
pp. 205873922110406
Author(s):  
Kürşad Ramazan Zor ◽  
Tuğba Arslan Gülen ◽  
Gamze Yıldırım Biçer ◽  
Erkut Küçük ◽  
Ayfer İmre ◽  
...  
Keyword(s):  
Nerve Fiber ◽  
Retinal Nerve Fiber Layer ◽  
Choroidal Thickness ◽  
Control Group ◽  
Fiber Layer ◽  
Subfoveal Choroidal Thickness ◽  
Nerve Fiber Layer ◽  
Rnfl Thickness ◽  
The Mean ◽  
Retinal Nerve Fiber

Introduction This study aims to detect changes in choroidal thickness and retinal nerve fiber layer (RNFL) thickness in acute stage brucellosis. Methods Fnewly diagnosed patients with acute brucellosis and 19 healthy individuals as control group were included in the study. Choroidal thickness and RNFL thickness were measured using the Spectral Domain Cirrus OCT Model 400 (Carl Zeiss Meditec, Jena, Germany) for each participant in the patient and control group. Results In the brucella group, in the right eyes, the mean nasal choroidal thickness was 272.77 ± 50.26 μm ( p = 0.689), the mean subfoveal choroidal thickness was 321.14 ± 33.08 μm ( p = 0.590), the mean temporal choroidal thickness was 278.86 ± 48.84 μm ( p = 0.478), and the mean RNFL thickness was 90.43 ± 8.93 μm ( p = 0.567). In the left eyes, the mean nasal choroidal thickness was 282.29 ± 48.93 μm ( p = 0.715), the mean subfoveal choroidal thickness was 316.79 ± 39.57 μm ( p = 0.540), the mean temporal choroidal thickness was 284.93 ± 50.57 μm ( p = 0.392), and the mean RNFL thickness was 92.64 ± 8.95 μm ( p = 0.813). Conclusion No difference was found between the control and the brucella groups regarding to all choroidal regions and RNFL thickness.

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