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This study evaluated the clinical outcomes of selective retina therapy (SRT) for treating central serous chorioretinopathy. A fundus image-based titration method was used for laser irradiation. This retrospective cohort study included 29 eyes (29 patients) that underwent SRT for CSC. Both the pulse energy and number of micropulses were adjusted according to the fundus image. Mean best-corrected visual acuity (BCVA), central foveal thickness (CFT), and subretinal fluid (SRF) height were measured 1, 2, 3, 4, and 6 months after SRT. Mean deviation (MD) was measured using microperimetry at 3 and 6 months post-treatment. At 6 months after SRT treatment, SRF was completely resolved in 89.7% of cases (26/29 eyes). The mean Snellen BCVA significantly improved from 0.34 ± 0.31 logMAR (logarithm of the minimum angle of resolution) (20/40) at baseline to 0.24 ± 0.24 logMAR (20/32) at 6 months ( = 0.009). The 0.1 improvement in mean BCVA is equivalent to a 5-letter gain on the ETDRS chart. The mean CFT decreased significantly from 309.31 ± 81.6 μm at baseline to 211.07 ± 50.21 μm at 6 months ( < 0.001). The mean SRF height also decreased significantly from 138.36 ± 56.78 μm at baseline to 23.75 ± 61.19 μm at 6 months ( < 0.001). The mean MD was improved from -1.56 ± 1.47 dB at baseline to -1.03 ± 2.43 dB at 6 months ( = 0.07) after treatment. SRT using fundus image-based titration can yield favorable functional and anatomical outcomes in the treatment of CSC.
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http://dx.doi.org/10.3390/jcm13175230 | DOI Listing |
Exp Eye Res
August 2025
Department of Computer Engineering, Sivas University of Science and Technology, Sivas, 58030, Turkiye. Electronic address:
This study introduces an automated diagnostic framework that detects diabetic retinopathy, glaucoma, and healthy retinas in color fundus images. It leverages several transfer learning (TL) backbones-DenseNet121, ResNet50, ResNet101V2, InceptionResNetV2, and Xception-augmented with additional dense layers, whose architecture and key training hyperparameters are optimized by the Grey Wolf Optimizer (GWO). To enhance image quality and improve feature visibility, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing, followed by data augmentation techniques such as rotations, shifts, and flips to reduce overfitting.
View Article and Find Full Text PDFJ Imaging
August 2025
Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia.
Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes a deep learning-based classification model using a pretrained EfficientNetB3 architecture, fine-tuned on a publicly available Kaggle retinal image dataset.
View Article and Find Full Text PDFInt Ophthalmol
August 2025
Department of Computer Science and Engineering, GITAM School of Technology, GITAM University, Bengaluru, Karnataka, 561203, India.
Objective: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidentified, and often takes a long time to get the right conclusions.
View Article and Find Full Text PDFUnlabelled: The retinal age gap (RAG; the difference between the retina's biological and chronological age) has recently gained increased attention as a potential image-based, non-invasive, and accessible biomarker for a broad spectrum of ocular and non-ocular diseases. Traditionally, machine learning predictions of biological retinal age utilize convolutional neural network (CNN) architectures and data from color fundus photography (CFP). Despite being previously unexplored, the multimodal fusion of two-dimensional CFP with three-dimensional optical coherence tomography (OCT) data has significant potential to enhance retinal age prediction accuracy and the diagnostic utility of the RAG biomarker.
View Article and Find Full Text PDFSensors (Basel)
July 2025
ICAR-CNR-Institute for High Performance Computing and Networking, National Research Council of Italy, Via P. Bucci 8/9C, 87036 Rende, Italy.
Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports.
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