Background: This study compares the change of retinal vessel density (VD) after pan-retinal photocoagulation (PRP) and intravitreal conbercept (IVC) treatment in proliferative diabetic retinopathy (PDR) eyes with optical coherence tomography angiography (OCTA). Methods: A total of 55 treatment-naïve PDR eyes were included in this retrospective study. Of these, 29 eyes were divided into a PRP group, and 26 eyes were divided into an IVC group based on the treatment they received. OCTA was performed to measure macular and papillary VD at each follow-up in both groups. Results: The macular VD for superficial capillary plexus (SCP), deep capillary plexus (DCP), choriocapillaris (CC) and papillary VD for radial peripapillary capillary (RPC) between the two groups demonstrated no significant difference at baseline and month 12 (p > 0.05). The paired t-test results showed that the macular VD for SCP, DCP, CC and papillary VD for the RPC at month 12 did not differ to the baseline in each group (p > 0.05). Conclusions: During the 12-month follow-up, there was no significant change of macular and papillary VD between the PRP and IVC treatment in PDR eyes. Additionally, compared to the baseline, there were no significant changes of macular and papillary VD after either the PRP or IVC treatment. Considering the decrease in VD as DR progress, both treatments have potential protection of macular and papillary VD loss in PDR.
Background: Machine learning was used to predict subretinal fluid absorption (SFA) at 1, 3 and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC).
Methods:The clinical and imaging data from 480 eyes of 461 patients with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data included clinical features from electronic medical records and measured features from fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography angiography (OCTA), and optical coherence tomography (OCT). A ZOC dataset was used for training and internal validation. An XEC dataset was used for external validation. Six machine learning algorithms and a blending algorithm were trained to predict SFA in patients with CSC after laser treatment. The SFA results predicted by machine learning were compared with the actual patient prognoses. Based on the initial detailed investigation, we constructed a simplified model using fewer clinical features and OCT features for convenient application.Results: During the internal validation, random forest performed best in SFA prediction, with accuracies of 0.651±0.068, 0.753±0.065 and 0.818±0.058 at 1, 3 and 6 months, respectively. In the external validation, XGBoost performed best at SFA prediction with accuracies of 0.734, 0.727, and 0.900 at 1, 3 and 6 months, respectively. The simplified model showed a comparable level of predictive power.Conclusions: Machine learning can achieve high accuracy in long-term SFA predictions and identify the features relevant to CSC patients' prognoses. Our study provides an individualized reference for ophthalmologists to treat and create a follow-up schedule for CSC patients.
Subretinal fluid (SRF) can lead to irreversible visual loss in patients with central serous chorioretinopathy (CSC) if not absorbed in time. Early detection and intervention of SRF can help improve visual prognosis and reduce irreversible damage to the retina. As fundus image is the most commonly used and easily obtained examination for patients with CSC, the purpose of our research is to investigate whether and to what extent SRF depicted on fundus images can be assessed using deep learning technology. In this study, we developed a cascaded deep learning system based on fundus image for automated SRF detection and macula-on/off serous retinal detachment discerning. The performance of our system is reliable, and its accuracy of SRF detection is higher than that of experienced retinal specialists. In addition, the system can automatically indicate whether the SRF progression involves the macula to provide guidance of urgency for patients. The implementation of our deep learning system could effectively reduce the extent of vision impairment resulting from SRF in patients with CSC by providing timely identification and referral.
Purpose: To report the efficacy and safety profile of subthreshold pan-retinal photocoagulation (PRP) using endpoint management (EPM) algorithm compared with conventional threshold PASCAL PRP for the treatment of severe non-proliferative diabetic retinopathy (NPDR).
Methods: This was a prospective, single center, paired randomized controlled trial of fifty-six eyes of twenty-eight participants with bilateral symmetric severe NPDR. One eye of the participant was randomly assigned to receive the subthreshold EPM PRP, while the other eye of the same participant received the threshold PASCAL PRP. The primary outcome measures included the difference in the one-year risk of progression to PDR between two groups, and mean changes of the logarithm of the minimal angle of resolution (logMAR) visual acuity (VA). The second outcome measures included central foveal thickness (CFT), one-year risk of progression to PDR, and visual field (VF) parameters.
Results: The subthreshold EPM PRP group and the threshold PASCAL PRP group had similar one-year risk of progression to PDR during the 12-month follow-up visits (17.86% vs 14.29%, P>0.05). Slightly decreased VA was found in both groups (0.08 vs 0.09 logMAR VA), however, no statistical difference was found for neither group (P>0.05). Similar results were found for thickened CFT for both groups (23.59μm vs 28.34μm, P>0.05). Specifically, although substantial loss of VF was found in the threshold PASCAL PRP group (P<0.05), no obvious damage to VF was seen in the subthreshold EPM PRP group (P>0.05).
Conclusion: The subthreshold EPM PRP is non-inferior to the conventional threshold PASCAL PRP in the treatment of severe NPDR during 12-month follow-up and could be an alternative treatment option for patients with severe NPDR.
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