Influence of Pilocarpine Eyedrops on the Ocular Biometric Parameters and Intraocular Lens Power Calculation.

Objective: To evaluate the influence of pilocarpine eyedrops on the ocular biometric parameters and whether these parameter changes affect the intraocular lens (IOL) power calculation in patients with primary angle-closure glaucoma (PACG). Methods: Twenty-two PACG patients and fifteen normal subjects were enrolled. Ocular biometric parameters including the axial length (AL), anterior chamber depth (ACD), lens thickness (LT), mean keratometry (Km), and white-to-white distance (WTW) were measured by using a Lenstar LS 900 device before and at least 30 minutes after instillation of 2% pilocarpine eyedrops. Lens position (LP) was calculated, and the IOL power prediction based on the ocular biometric parameters was performed using the Barrett Universal II, Haigis, Hoffer Q, Holladay I, or SRK/T formulas before and after pilocarpine application. Results: In both PACG and normal groups, pilocarpine eyedrops induced a slight but statistically significant increase in the mean AL (0.01 mm for both groups) and mean LT (0.02 mm and 0.03 mm, respectively) but a significant decrease in the mean ACD (0.03 mm and 0.05 mm, respectively) and mean LP (0.02 mm and 0.04 mm, respectively). No significant changes in the mean Km and WTW were noticed in both groups. In addition, the IOL power calculation revealed insignificant changes before and after the pilocarpine instillation in both groups, regardless of the formula used. Conclusions: Pilocarpine eyedrops can induce slight changes in the ocular biometric parameters including the AL, ACD, LT, and LP. However, these parameter changes will not result in a significant difference in IOL power estimation.

Ocular Adverse Events after Inactivated COVID-19 Vaccination in Xiamen.

Aims: To report potential vaccine-induced ocular adverse events following inactivated COVID-19 vaccination (Sinopharm and Sinovac). Methods: This case series took place at a tertiary referral center in the southeast of China (Xiamen Eye Center in Fujian Province) from February 2021 to July 2021. Patients who received the first dose of inactivated COVID-19 vaccine and developed vaccine-related ocular adverse events within 10 days were included. The diagnosis of vaccine-related ocular adverse events was guided by the World Health Organization causality assessment and the Naranjo criteria. Results: Ten eyes of seven patients (two male individuals) presenting with ocular complaints following COVID-19 vaccine were included in the study. The mean (SD) age was 41.4 (9.3) years (range, 30-55 years). The mean time of ocular adverse event manifestations was 4.9 days (range, 1-10 days). Three patients were diagnosed with Vogt-Koyanagi-Harada (VKH)-like uveitis, one with multifocal choroiditis, one with episcleritis, one with iritis, and one with acute idiopathic maculopathy. Two patients received the second dose of vaccine. One patient had exacerbation of VKH, and one patient had no symptoms. An aqueous humor analysis in three patients revealed elevated proinflammatory cytokines and negative virus copy. All the patients had transient ocular disturbance and responded well to steroids. No recurrence was noted during 6 months of follow-up. Conclusions: Potential ocular adverse events should be reported to increase the awareness of the health community for timely detection and proper treatment.

Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning.

Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning. Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features. Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power. Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.

Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence.

To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074-0.098 logMAR (within four to five letters), and the root mean square errors were 0.096-0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 µm and 22.46 ± 9.71 µm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.

Predicting subretinal fluid absorption with machine learning in patients with central serous chorioretinopathy.

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.

Decreased Circulating Levels of Dickkopf-1 in Patients with Exudative Age-related Macular Degeneration.

Aberrant activation of the Wnt/ß-catenin signaling pathway plays a pathogenic role in retinal inflammation and neovascularization. Here, we investigated whether circulating levels of Dickkopf-1 (DKK-1), a specific inhibitor of this pathway, are altered in patients with exudative age-related macular degeneration (AMD). Plasma was obtained from 128 patients with exudative AMD, 46 patients with atrophic AMD and 111 healthy controls. DKK-1 levels in plasma were measured using ELISA, and data analyzed with one-way ANOVA, logistic regression analysis and receiver-operating characteristic analysis (ROC). We found that DKK-1 levels were decreased in exudative AMD patients, compared with healthy controls (P < 0.001) and atrophic AMD patients (P < 0.001). The decrease was more prominent in patients with classic choroidal neovascularization (CNV) than those with occult CNV (P < 0.001). The odds ratio (OR) of exudative AMD was 11.71 (95% CI; 5.24-6.13) for lowest versus upper quartile of DKK-1 levels. For discriminating exudative AMD patients, the optimum diagnostic cutoff of DKK-1 was 583.1 pg/mL with the area under curve (AUC) 0.76 (95% CI, 0.70-0.82; P < 0.001), sensitivity 78.1% and specificity 63.1%. These findings suggested that decreased circulating DKK-1 levels are associated with the development and severity of exudative AMD, and have potential to become a biomarker for exudative AMD.