AIMC Topic: Diagnostic Techniques, Ophthalmological

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Privacy-Preserving Federated Learning With Domain Adaptation for Multi-Disease Ocular Disease Recognition.

IEEE journal of biomedical and health informatics
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
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
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
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
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...

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

IEEE transactions on medical imaging
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models f...

Diagnostic accuracy of artificial intelligence in detecting retinitis pigmentosa: A systematic review and meta-analysis.

Survey of ophthalmology
Retinitis pigmentosa (RP) is often undetected in its early stages. Artificial intelligence (AI) has emerged as a promising tool in medical diagnostics. Therefore, we conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy ...

Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs.

PloS one
Deep learning (DL) models have shown promise in detecting chronic kidney disease (CKD) from fundus photographs. However, previous studies have utilized a serum creatinine-only estimated glomerular rate (eGFR) equation to measure kidney function despi...

Artificial intelligence for glaucoma: state of the art and future perspectives.

Current opinion in ophthalmology
PURPOSE OF REVIEW: To address the current role of artificial intelligence (AI) in the field of glaucoma.

Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.

Medical & biological engineering & computing
Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distr...