Comparison of effects of tamsulosin and silodosin on subfoveal choroidal thickness and pupil size diameters in patients with prostatic hyperplasia

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.

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

Applied Sciences ◽

10.3390/app11125488 ◽

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.

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