AIMC Topic: Retinal Diseases
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A deep learning model for diagnosis of inherited retinal diseases.
Scientific reports
Jul 2, 2025
To evaluate the performance of a multi-input deep learning (DL) model in detecting two common inherited retinal diseases (IRDs), i.e. retinitis pigmentosa (RP) and Stargardt disease (STGD), and differentiating them from healthy eyes. This cross-secti...
Realistic fundus photograph generation for improving automated disease classification.
The British journal of ophthalmology
Jun 23, 2025
AIMS: This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep convolutional neural network (CNN) ensemble for mult...
The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study.
BMC medical education
Apr 8, 2025
OBJECTIVE: To assess the potential of artificial intelligence reading label system on the training of ophthalmologists in a multicenter bimodal multi-disease study.
Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 4, 2025
Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT ima...
Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for ...
Integrating lightweight convolutional neural network with entropy-informed channel attention and adaptive spatial attention for OCT-based retinal disease classification.
Computers in biology and medicine
Apr 1, 2025
This article proposes an effective and lightweight contextual convolutional neural network architecture called LOCT-Net for classifying retinal diseases. The LOCT-Net adopts nested residual blocks to capture the local patterns from the optical cohere...
AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed.
Progress in retinal and eye research
Mar 12, 2025
Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic diseases from retinal images. Unlike traditional retinal disease detection AI models which are trained ...
Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.
Wiener medizinische Wochenschrift (1946)
Feb 24, 2025
Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introduction of artificial intelligence (AI) into this field has been a logical, inevitable, and successful...
A novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images.
Medical & biological engineering & computing
Feb 7, 2025
Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends on the exp...
Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 1, 2025
The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. Identifying these conditions promptly guarantees early treatment and prevents blindness. However, the challenge lies in accurately diagnosing these co...
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