AIMC Topic: Diagnostic Techniques, Ophthalmological
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AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.
IEEE transactions on medical imaging
Jan 2, 2024
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
Dec 1, 2023
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
Nov 30, 2023
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
Nov 29, 2023
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
Oct 27, 2023
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...
Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer.
Computers in biology and medicine
Oct 25, 2023
Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that in...
Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology.
Ocular immunology and inflammation
Oct 13, 2023
PURPOSE: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practic...
Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis.
Ocular immunology and inflammation
Sep 18, 2023
INTRODUCTION: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice...
A nystagmus extraction system using artificial intelligence for video-nystagmography.
Scientific reports
Jul 24, 2023
Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography ha...
Sex determination using color fundus parameters in older adults of Kumejima population study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Mar 1, 2023
PURPOSE: Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex ...
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