Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence

Central serous chorioretinopathy: An evidence-based treatment guideline

Central serous chorioretinopathy (CSC) is a relatively common disease that causes vision loss due to macular subretinal fluid leakage and it is often associated with reduced vision-related quality of life. In CSC, the leakage of subretinal fluid through defects in the retinal pigment epithelial layer's outer blood-retina barrier appears to occur secondary to choroidal abnormalities and dysfunction. The treatment of CSC is currently the subject of controversy, although recent data obtained from several large randomized controlled trials provide a wealth of new information that can be used to establish a treatment algorithm. Here, we provide a comprehensive overview of our current understanding regarding the pathogenesis of CSC, current therapeutic strategies, and an evidence-based treatment guideline for CSC. In acute CSC, treatment can often be deferred for up to 3-4 months after diagnosis; however, early treatment with either half-dose or half-fluence photodynamic therapy (PDT) with the photosensitive dye verteporfin may be beneficial in selected cases. In chronic CSC, half-dose or half-fluence PDT, which targets the abnormal choroid, should be considered the preferred treatment. If PDT is unavailable, chronic CSC with focal, non-central leakage on angiography may be treated using conventional laser photocoagulation. CSC with concurrent macular neovascularization should be treated with half-dose/half-fluence PDT and/or intravitreal injections of an anti-vascular endothelial growth factor compound. Given the current shortage of verteporfin and the paucity of evidence supporting the efficacy of other treatment options, future studies-ideally, well-designed randomized controlled trials-are needed in order to evaluate new treatment options for CSC.

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.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

A Photodynamic Therapy Index for Central Serous Chorioretinopathy to Predict Visual Prognosis Using Pretreatment Factors

PURPOSE

This study aimed to establish a treatment index based on functional outcomes of photodynamic therapy (PDT) for central serous chorioretinopathy (CSC).

DESIGN

A retrospective clinical case-control study.

METHODS

This was a single-institute study. Eighty (80) eyes with CSC, who were treated by PDT and whose subretinal fluid resolves within 6 months were divided into two groups: those with poor visual outcome (PVO) (best-corrected visual acuity [BCVA] ≤ 0.5 6 months post-PDT), and the remaining eyes (better visual outcome [BVO]). The areas under the curve (AUC) and cutoff values from receiver operating characteristic curves were examined. These was used to predict the groups using pretreatment BCVA and the thickness of each retinochoroidal layer.

RESULT

Twenty-one (21) eyes were in the PVO group and 59 eyes in the BVO group were included. The AUCs were 0.959 for BCVA, 0.959 for the thickness from the internal limiting membrane to the external limiting membrane (IET), 0.820 for the thickness from the external limiting membrane to the photoreceptor outer segment layer, 0.715 for the subfoveal retinal thickness, and 0.515 for the subfoveal choroidal thickness. The BCVA and IET cut-off values were 0.267 logMAR and 71.5 µm, respectively. Using the combination of the cutoff values of BCVA and IET, the highest values for the sensitivity, specificity, positive predictive value, and negative predictive value were 95.2%, 94.9%, 85.0%, and 98.0%, respectively.

CONCLUSION

The combination of pre-PDT BCVA and IET in CSC can accurately predict the post-treatment visual prognosis. These values could be used as a treatment index of PDT for CSC.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.