AIMC Topic: Tomography, Optical Coherence
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Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN.
IEEE journal of biomedical and health informatics
Apr 4, 2025
For optical coherence tomography angiography (OCTA) images, the limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging resolution. Although larger FOV images may reveal more parafoveal vascular lesions, their application i...
Integrating lightweight convolutional neural network with entropy-informed channel attention and adaptive spatial attention for OCT-based retinal disease classification.
Computers in biology and medicine
Apr 1, 2025
This article proposes an effective and lightweight contextual convolutional neural network architecture called LOCT-Net for classifying retinal diseases. The LOCT-Net adopts nested residual blocks to capture the local patterns from the optical cohere...
Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.
The British journal of ophthalmology
Mar 20, 2025
BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We established the normative distribution of anterior chamber depth (ACD), area (ACA) and width (ACW) and lens ...
Monitoring kidney microanatomy during ischemia-reperfusion using ANFIS optimized CNN.
International urology and nephrology
Mar 18, 2025
Kidney disease is a dangerous disease that affects human health and causes various defects. Renal microbiological changes can be monitored using optical coherence tomography (OCT) images to identify the nature of the disease based on behavior during ...
Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.
Frontiers in endocrinology
Mar 18, 2025
OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).
Automated chick gender determination using optical coherence tomography and deep learning.
Poultry science
Mar 15, 2025
Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by human expertise, labor intensity, and insufficient resolution. This study introduces a novel approa...
Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study.
International journal of cardiology
Mar 9, 2025
BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.
Prediction of Postoperative Macular Hole Status by Automated Preoperative Retinal OCT Analysis: A Narrative Review.
Ophthalmic surgery, lasers & imaging retina
Mar 1, 2025
Optical coherence tomography (OCT) is a non-invasive imaging modality essential for macular hole (MH) management. Artificial intelligence (AI) algorithms could be applied to OCT to garner insights for MH prognosis and outcomes. The objective was to r...
Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography Using Deep Learning.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Mar 1, 2025
PURPOSE: To report a deep learning neural network on anterior segment optical coherence tomography (AS-OCT) for automated detection of different keratorefractive laser surgeries-including laser in situ keratomileusis with femtosecond microkeratome (f...
Segmentation of 3D OCT Images of Human Skin Using Neural Networks with U-Net Architecture.
Sovremennye tekhnologii v meditsine
Feb 28, 2025
UNLABELLED: is a comparative analysis of algorithms for segmentation of three-dimensional OCT images of human skin using neural networks based on U-Net architecture when training the model on two-dimensional and three-dimensional data.
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