AIMC Topic: Diagnostic Techniques, Ophthalmological
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Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.
The British journal of ophthalmology
Sep 20, 2024
BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserv...
Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.
Survey of ophthalmology
Jul 23, 2024
Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Co...
Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis.
Cornea
Jul 9, 2024
PURPOSE: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations.
AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.
Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
Jul 5, 2024
BACKGROUND: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and ...
Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.
Survey of ophthalmology
Jun 15, 2024
Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integ...
Privacy-Preserving Federated Learning With Domain Adaptation for Multi-Disease Ocular Disease Recognition.
IEEE journal of biomedical and health informatics
Jun 6, 2024
As one of the effective ways of ocular disease recognition, early fundus screening can help patients avoid unrecoverable blindness. Although deep learning is powerful for image-based ocular disease recognition, the performance mainly benefits from a ...
Generative artificial intelligence in ophthalmology.
Survey of ophthalmology
May 16, 2024
Generative artificial intelligence (AI) has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep learning framework that has become a powerful technique in medicine, particularly in ophthalmology for i...
SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.
IEEE journal of biomedical and health informatics
May 6, 2024
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised imag...
Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.
European journal of ophthalmology
Apr 24, 2024
To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Two-hundred one patients (mean age ...
Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.
BMC medical informatics and decision making
Jan 26, 2024
BACKGROUND: The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become in...
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