AIMC Topic: Fundus Oculi
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Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.
Eye (London, England)
May 11, 2024
BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening po...
Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography.
Scientific reports
May 11, 2024
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis of patients with branch retinal vein occlusion (BRVO). However, the current evaluation method of NPA, fluorescein angiography (FA), is invasive and burdensom...
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...
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