AIMC Topic: Macular Edema

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Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

Biomedical engineering online
BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well...

Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms.

Irish journal of medical science
BACKGROUND: The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. ...

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

Eye (London, England)
PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five ste...

Multimodal Imaging in Diabetic Macular Edema.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Throughout ophthalmic history it has been shown that progress has gone hand in hand with technological breakthroughs. In the past, fluorescein angiography and fundus photographs were the most commonly used imaging modalities in the management of diab...

[Adaptive lesion-aware fusion network for joint grading of multiple fundus diseases].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Diabetic retinopathy (DR) and its complication, diabetic macular edema (DME), are major causes of visual impairment and even blindness. The occurrence of DR and DME is pathologically interconnected, and their clinical diagnoses are closely related. J...

Validation of a deep learning model for the automated detection and quantification of cystoid macular oedema on optical coherence tomography in patients with retinitis pigmentosa.

Acta ophthalmologica
PURPOSE: Accurate assessment of cystoid macular oedema (CMO) in patients with retinitis pigmentosa (RP) on spectral-domain optical coherence tomography (SD-OCT) is crucial for tracking disease progression and may serve as a therapeutic endpoint. Manu...

Integration of proteomics and artificial intelligence-driven OCT biomarker analysis in central retinal vein occlusion.

Experimental eye research
Retinal OCT biomarker analysis by artificial intelligence (AI) has not previously been integrated with proteomics. Here, we combined the two techniques to elucidate novel molecular mechanisms in central retinal vein occlusion (CRVO). Proteomic data o...

Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images.

BMJ open
OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

Translational vision science & technology
PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.

Indian journal of ophthalmology
Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The...