Can subfoveal choroidal thickness replace subjective tests in patients using tadalafil to treat erectile dysfunction?

Andrologia ◽  
2020 ◽  
Vol 52 (6) ◽  
Author(s):  
Fatih Aslan ◽  
Murat Topcuoğlu ◽  
Çağlar Öktem ◽  
Ali Akkoç ◽  
Murat Uçar
Keyword(s):  
Erectile Dysfunction ◽  
Choroidal Thickness ◽  
Subfoveal Choroidal Thickness ◽  
Subjective Tests

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.

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 change following pars plana vitrectomy with silicone oil endotamponade for rhegmatogenous retinal detachment

2018 ◽  
Vol 39 (8) ◽  
pp. 1717-1722 ◽  
Author(s):  
Enver Mirza ◽  
Günhal Şatırtav ◽  
Refik Oltulu ◽  
Hürkan Kerimoğlu ◽  
Mehmet Kemal Gündüz
Keyword(s):  
Retinal Detachment ◽  
Pars Plana Vitrectomy ◽  
Choroidal Thickness ◽  
Rhegmatogenous Retinal Detachment ◽  
Silicone Oil ◽  
Thickness Change ◽  
Subfoveal Choroidal Thickness ◽  
Pars Plana

Subfoveal Choroidal Thickness in Fellow Eyes of Patients with Central Serous Chorioretinopathy

2012 ◽  
Vol 53 (7) ◽  
pp. 982 ◽  
Author(s):  
Seung Hyen Lee ◽  
Hyewon Chung ◽  
Hyung Chan Kim
Keyword(s):  
Central Serous Chorioretinopathy ◽  
Choroidal Thickness ◽  
Subfoveal Choroidal Thickness ◽  
Fellow Eyes

Evaluation of Subfoveal Choroidal Thickness in Pregnant Women Using Enhanced Depth Imaging Optical Coherence Tomography

2014 ◽  
Vol 39 (6) ◽  
pp. 642-647 ◽  
Author(s):  
Necip Kara ◽  
Nihat Sayin ◽  
Dilara Pirhan ◽  
Asli Deger Vural ◽  
Hatice Bilge Araz-Ersan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Pregnant Women ◽  
Choroidal Thickness ◽  
Enhanced Depth Imaging ◽  
Optical Coherence ◽  
Depth Imaging ◽  
Subfoveal Choroidal Thickness ◽  
Imaging Optical Coherence Tomography
Sign in / Sign up

Export Citation Format

Share Document