AIMC Topic: Fundus Oculi
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Automated machine learning model for fundus image classification by health-care professionals with no coding experience.
Scientific reports
May 6, 2024
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct da...
LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.
Physiological measurement
May 3, 2024
This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes ...
Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.
Medical & biological engineering & computing
Apr 23, 2024
Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermo...
Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.
Diabetes, obesity & metabolism
Apr 15, 2024
AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance d...
A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes - An exploratory analysis.
Primary care diabetes
Apr 14, 2024
AIMS: Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly....
Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
Computer methods and programs in biomedicine
Apr 3, 2024
BACKGROUND AND OBJECTIVE: Early detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening progra...
Recognition of Glaucomatous Fundus Images Using Machine Learning Methods Based on Optic Nerve Head Topographic Features.
Journal of glaucoma
Mar 29, 2024
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.
Predicting central choroidal thickness from colour fundus photographs using deep learning.
PloS one
Mar 29, 2024
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...
Real-world artificial intelligence-based interpretation of fundus imaging as part of an eyewear prescription renewal protocol.
Journal francais d'ophtalmologie
Mar 8, 2024
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).
Early detection of glaucoma integrated with deep learning models over medical devices.
Bio Systems
Feb 28, 2024
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...
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