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Optical coherence tomography-enabled classification of the human venoatrial junction.
J Biomed Opt
January 2025
Columbia University, Department of Electrical Engineering, New York, United States.
Significance: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.
Aim: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.
Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.
Sci Rep
January 2025
Hive AI Innovation Studio, Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes associated with a wide range of systemic diseases, such as diabetes, cardiovascular disorders, and rheumatological conditions like systemic sclerosis, can manifest as observable microvascular changes in the terminal capillaries of nailfolds. The current gold standard relies on experts performing manual evaluations, which is an exhaustive time-intensive, and subjective process.
View Article and Find Full Text PDFLRNet: Link Residual Neural Network for Blood Vessel Segmentation in OCTA Images.
J Imaging Inform Med
January 2025
Department of Ophthalmology, The Affiliated Hospital of Guilin Medical University, Guilin, China.
Optical coherence tomography angiography (OCTA) is an emerging, non-invasive technique increasingly utilized for retinal vasculature imaging. Analysis of OCTA images can effectively diagnose retinal diseases, unfortunately, complex vascular structures within OCTA images possess significant challenges for automated segmentation. A novel, fully convolutional dense connected residual network is proposed to effectively segment the vascular regions within OCTA images.
View Article and Find Full Text PDFA systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.
Neuroscience
January 2025
Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey. Electronic address:
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions.
View Article and Find Full Text PDFEvaluating the effect of noise reduction strategies in CT perfusion imaging for predicting infarct core with deep learning.
Neuroradiol J
January 2025
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.
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