AIMC Topic: Macular Edema
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Explainable ensemble learning method for OCT detection with transfer learning.
PloS one
Mar 22, 2024
The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects d...
Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.
Sensors (Basel, Switzerland)
Jul 26, 2023
Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however...
Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?
IEEE journal of translational engineering in health and medicine
Jul 24, 2023
UNLABELLED: - Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).
Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.
BMC ophthalmology
Sep 1, 2022
BACKGROUND: To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiograp...
Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.
IEEE journal of biomedical and health informatics
Aug 11, 2022
The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes i...
Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.
Sensors (Basel, Switzerland)
Jun 21, 2022
The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with ...
Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.
European journal of ophthalmology
Apr 27, 2022
PURPOSE: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.
Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.
Sensors (Basel, Switzerland)
Apr 15, 2022
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a p...
Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study.
The Lancet. Digital health
Mar 7, 2022
BACKGROUND: Diabetic retinopathy is a leading cause of preventable blindness, especially in low-income and middle-income countries (LMICs). Deep-learning systems have the potential to enhance diabetic retinopathy screenings in these settings, yet pro...
Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.
Contrast media & molecular imaging
Mar 4, 2022
This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group ( =...
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