2/6/2024 0 Comments Cardiac coherenceDifferentiation of diabetic status using statistical and machine learning techniques on optical coherence tomography angiography images. Automated OCT angiography image quality assessment using a deep learning algorithm. Lauermann JL, Treder M, Alnawaiseh M, Clemens CR, Eter N, Alten F. Artificial intelligence in OCT angiography. Hormel TT, Hwang TS, Bailey ST, Wilson DJ, Huang D, Jia Y. MobileNetV2: inverted residuals and linear bottlenecks. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L. Image artifacts in optical coherence tomography angiography. Retinal microvascular network and microcirculation assessments in high myopia. Li M, Yang Y, Jiang H, Gregori G, Roisman L, Zheng F, et al. Current status of clinical research using artificial intelligence techniques :a registry-based audit. Impact of cardiometabolic factors on retinal vasculature: A 3 × 3, 6 × 6 and 8 × 8-mm ocular coherence tomography angiography study. Sun MT, Huang S, Chan W, Craig JE, Knight LSW, Sanders P, et al. Evaluation of macular perfusion in healthy smokers by using optical coherence tomography angiography. 2019 9:10161.Īyhan Z, Kaya M, Ozturk T, Karti O, Hakan, Oner F. Immediate effects of smoking on optic nerve and macular perfusion measured by optical coherence tomography angiography. Optical coherence tomography angiography discerns preclinical diabetic retinopathy in eyes of patients with type 2 diabetes without clinical diabetic retinopathy. 2019 26:e12555.Ĭao D, Yang D, Huang Z, Zeng Y, Wang J, Hu Y, et al. Early retinal microvascular abnormalities in patients with chronic kidney disease. Yeung L, Wu IW, Sun CC, Liu CF, Chen SY, Tseng CH, et al. Changes in ganglion cell-inner plexiform layer thickness and retinal microvasculature in hypertension: an optical coherence tomography angiography study. Lim HB, Lee MW, Park JH, Kim K, Jo YJ, Kim JY. Impact of hypertension on retinal capillary microvasculature using optical coherence tomographic angiography. The EYE-MI pilot study: a prospective acute coronary syndrome cohort evaluated with retinal optical coherence tomography angiography. Early detection of risk factors prior to a clinically significant event, will assist in preventing adverse outcomes for people.Īrnould L, Guenancia C, Azemar A, Alan G, Pitois S, Bichat F, et al. This study demonstrates the strength of ML to identify the presence cardiometabolic factors, in particular hyperlipidaemia, in high-resolution 3 × 3 mm OCTA scans. There was no significant recognition for 6 × 6 and 8 × 8 mm for any cardiometabolic risk factor. Modest performance was achieved in the identification of diabetes mellitus, hypertension and congestive heart failure in 3 × 3 mm scans (all with AUC and accuracy >0.5). Both models performed best in predicting the presence of hyperlipidaemia in 3 × 3 mm scans with an AUC of 0.74 and 0.81, and accuracy of 0.79 for CNN and MobileNetV2 respectively. Two hundred forty-seven participants were included. Once developed on the training dataset, their performance was assessed on the unseen test dataset. The data was then pre-processed and randomly split into training and testing datasets (75%/25% split) before being applied to two models (Convolutional Neural Network and MoblieNetV2). Demographic and co-morbidity data was collected for each participant undergoing 3 × 3 mm, 6 × 6 mm and 8 × 8 mm OCTA scanning using the Carl Zeiss CIRRUS HD-OCT model 5000. The study aims to assess the feasibility of predicting the presence or absence of cardiovascular conditions and their associated risk factors using machine learning and OCTA. Machine learning has previously been applied within ophthalmic imaging but has not yet been applied to these risk factors. Optical coherence tomography angiography (OCTA) has been found to identify changes in the retinal microvasculature of people with various cardiometabolic factors.
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