PRCIS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features making it a straightforward and effective approach.
The estimation of central choroidal thickness from colour fundus images can improve disease detection. We developed a deep learning method to estimate central choroidal thickness from colour fundus images at a single institution, using independent da...
OBJECTIVE: A real-world evaluation of the diagnostic accuracy of the Opthai® software for artificial intelligence-based detection of fundus image abnormalities in the context of the French eyewear prescription renewal protocol (RNO).
The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindn...
PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This study assesses the performance of AutoML in detecting and localizing ocular toxoplasmosis (OT) lesions in fund...
In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of ...
AIMS: To develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.
Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnosti...
Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an u...
OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).