Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning

BASELINE SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHIC RETINAL LAYER FEATURES IDENTIFIED BY ARTIFICIAL INTELLIGENCE PREDICT THE COURSE OF CENTRAL SEROUS CHORIORETINOPATHY

PURPOSE

To identify optical coherence tomography (OCT) features to predict the course of central serous chorioretinopathy (CSC) with an artificial intelligence-based program.

METHODS

Multicenter, observational study with a retrospective design. Treatment-naïve patients with acute CSC and chronic CSC were enrolled. Baseline OCTs were examined by an artificial intelligence-developed platform (Discovery OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). Through this platform, automated retinal layer thicknesses and volumes, including intaretinal and subretinal fluid, and pigment epithelium detachment were measured. Baseline OCT features were compared between acute CSC and chronic CSC patients.

RESULTS

One hundred and sixty eyes of 144 patients with CSC were enrolled, of which 100 had chronic CSC and 60 acute CSC. Retinal layer analysis of baseline OCT scans showed that the inner nuclear layer, the outer nuclear layer, and the photoreceptor-retinal pigmented epithelium complex were significantly thicker at baseline in eyes with acute CSC in comparison with those with chronic CSC ( P < 0.001). Similarly, choriocapillaris and choroidal stroma and retinal thickness (RT) were thicker in acute CSC than chronic CSC eyes ( P = 0.001). Volume analysis revealed average greater subretinal fluid volumes in the acute CSC group in comparison with chronic CSC ( P = 0.041).

CONCLUSION

Optical coherence tomography features may be helpful to predict the clinical course of CSC. The baseline presence of an increased thickness in the outer retinal layers, choriocapillaris and choroidal stroma, and subretinal fluid volume seems to be associated with acute course of the disease.

FunSwin: A deep learning method to analysis diabetic retinopathy grade and macular edema risk based on fundus images

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient’s quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.

Factors for recurrence in acute central serous chorioretinopathy patients underwent one-third dose verteporfin photodynamic therapy

PURPOSE

To investigate the factors of recurrence in acute central serous chorioretinopathy (aCSC) patients underwent one-third dose verteporfin photodynamic therapy (PDT).

MATERIALS AND METHODS

A retrospective study was performed in aCSC patients treated with one-third dose PDT and followed up for 36 months. Demographic information and clinical features were compared. A logistic regression model was used to evaluate the associated factor of aCSC recurrence.

RESULTS

There were 162 patients with aCSC included in current study. 36-month after one-third dose PDT, good recovery was identified in 131 patients (80.86%), whereas 31 cases (19.14%) developed recurrence. Significant between-group differences were observed in baseline age, the right, left and both eyes, best-corrected visual acuity (BCVA), presenting with pigment epithelium detachment (PED), retinal pigment epithelium (RPE) damage and subfoveal choroidal thickness (SCT) level (P = 0.005, P < 0.001, P < 0.001, P < 0.001, P = 0.006, and P < 0.001, respectively). The recurrence of aCSC was associated with presenting with PED (odds ratio = 1.78; 95% CI, 1.45 - 1.98; P < 0.001), RPE damage (OR = 1.13; 95%CI: 1.08 - 1.23; P < 0.001), baseline BCVA (OR = 0.96; 95%CI: 0.95 - 0.99; P = 0.001), and SCT level (OR = 1.18; 95%CI: 1.02 - 1.20; P < 0.001).

CONCLUSION

In acute CSC after treatment of one-third dose PDT, recurrence is associated with RPE damage, baseline BCVA and SCT level. Our findings will assist clinicians to evaluate aCSC in clinical practice and provide insights into the prevention of recurrence.