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Chinese Medical Journal Jun 2024
PubMed: 38879807
DOI: 10.1097/CM9.0000000000003166 -
BMJ Case Reports Sep 2023Neurofibromatosis type 1 (NF1) is known to cause vascular complications including stenotic and aneurysmal disease. Here, we report a case of a woman in her early 20s,...
Neurofibromatosis type 1 (NF1) is known to cause vascular complications including stenotic and aneurysmal disease. Here, we report a case of a woman in her early 20s, who presented with unilateral facial nerve palsy and hypertensive crisis, and was later found to have multiple bilateral intrarenal microaneurysms along with classic cutaneous manifestations of NF1. A causal relationship between the pathophysiology of NF1 and the development of renal artery microaneurysm and hypertension is proposed in this case report.
Topics: Female; Humans; Neurofibromatosis 1; Hypertension; Cardiovascular Diseases; Bell Palsy; Facial Paralysis
PubMed: 37669821
DOI: 10.1136/bcr-2022-254012 -
Heliyon Jul 2023Accurate diabetic retinopathy (DR) grading is crucial for making the proper treatment plan to reduce the damage caused by vision loss. This task is challenging due to...
Accurate diabetic retinopathy (DR) grading is crucial for making the proper treatment plan to reduce the damage caused by vision loss. This task is challenging due to the fact that the DR related lesions are often small and subtle in visual differences and intra-class variations. Moreover, relationships between the lesions and the DR levels are complicated. Although many deep learning (DL) DR grading systems have been developed with some success, there are still rooms for grading accuracy improvement. A common issue is that not much medical knowledge was used in these DL DR grading systems. As a result, the grading results are not properly interpreted by ophthalmologists, thus hinder the potential for practical applications. This paper proposes a novel fine-grained attention & knowledge-based collaborative network (FA+KC-Net) to address this concern. The fine-grained attention network dynamically divides the extracted feature maps into smaller patches and effectively captures small image features that are meaningful in the sense of its training from large amount of retinopathy fundus images. The knowledge-based collaborative network extracts medical knowledge features, i.e., lesions such as the microaneurysms (MAs), soft exudates (SEs), hard exudates (EXs), and hemorrhages (HEs). Finally, decision rules are developed to fuse the DR grading results from the fine-grained network and the knowledge-based collaborative network to make the final grading. Extensive experiments are carried out on four widely-used datasets, the DDR, Messidor, APTOS, and EyePACS to evaluate the efficacy of our method and compare with other state-of-the-art (SOTA) DL models. Simulation results show that proposed FA+KC-Net is accurate and stable, achieves the best performances on the DDR, Messidor, and APTOS datasets.
PubMed: 37449186
DOI: 10.1016/j.heliyon.2023.e17217 -
Ocular Immunology and Inflammation Mar 2024Systemic sclerosis (SSc) is a chronic multisystemic disease characterized by immunological activation, diffuse vasculopathy, and generalized fibrosis exhibiting a... (Review)
Review
Systemic sclerosis (SSc) is a chronic multisystemic disease characterized by immunological activation, diffuse vasculopathy, and generalized fibrosis exhibiting a variety of symptoms. A recognized precursor of SSc is Raynaud's phenomenon, which is part of the very early disease of systemic sclerosis (VEDOSS) in combination with nailfold videocapillaroscopy (NVC) impairment. The pathophysiology of ocular involvement, alterations in internal organs, and body integumentary system involvement in SSc patients are complicated and poorly understood, with multiple mechanisms presumptively working together. The most prevalent ocular symptoms of SSc are abnormalities of the eyelids and conjunctiva as well as dry eye syndrome, due to fibroblasts' dysfunction and inflammation of the ocular surface. In particular, lagophthalmos, blepharophimosis limitation of eyelid motion, eyelid telangiectasia, and rigidity or tightening of the lids may affect up to two-third of the patients. In addition, reduction in central corneal thickness, iris defects and higher rates of glaucoma were reported. In the first reports based on retinography or fluorescein angiography, about 50% of SSc patients showed signs of vascular disease: peripheral artery occlusion, thinning of retinal pigment epithelium and choroidal capillaries, ischemic areas surrounded by intraretinal extravasation and microaneurysms, and peripheral capillary non-perfusion. Successively, thanks to the advent of optical coherence tomography angiography (OCTA), several studies highlighted significant impairment of either the choriocapillaris and retinal vascular plexuses, also correlating with NVC involvement and skin disease, even in VEDOSS disease. Given the sensitivity of this technique, ocular micro-vasculopathy may act as a tool for early SSc identification and discriminate between disease stages.
PubMed: 38466107
DOI: 10.1080/09273948.2024.2308030 -
Diagnostics (Basel, Switzerland) Nov 2023Diseases such as diabetes affect the retinal vasculature and the health of the neural retina, leading to vision problems. We describe here an imaging method and analysis...
Diseases such as diabetes affect the retinal vasculature and the health of the neural retina, leading to vision problems. We describe here an imaging method and analysis procedure that enables characterization of the retinal vessel walls with cellular-level resolution, potentially providing markers for eye diseases. Adaptive optics scanning laser ophthalmoscopy is used with a modified detection scheme to include four simultaneous offset aperture channels. The magnitude of the phase gradient derived from these offset images is used to visualize the structural characteristics of the vessels. The average standard deviation image provides motion contrast and enables segmentation of the vessel lumen. Segmentation of blood vessel walls provides quantitative measures of geometrical characteristics of the vessel walls, including vessel and lumen diameters, wall thickness, and wall-to-lumen ratio. Retinal diseases may affect the structural integrity of the vessel walls, their elasticity, their permeability, and their geometrical characteristics. The ability to measure these changes is valuable for understanding the vascular effects of retinal diseases, monitoring disease progression, and drug testing. In addition, loss of structural integrity of the blood vessel wall may result in microaneurysms, a hallmark lesion of diabetic retinopathy, which may rupture or leak and further create vision impairment. Early identification of such structural abnormalities may open new treatment avenues for disease management and vision preservation. Functional testing of retinal circuitry through high-resolution measurement of vasodilation as a response to controlled light stimulation of the retina (neurovascular coupling) is another application of our method and can provide an unbiased evaluation of one's vision and enable early detection of retinal diseases and monitoring treatment results.
PubMed: 37998535
DOI: 10.3390/diagnostics13223399 -
Scientific Reports Nov 2023Coats' disease is an idiopathic retinal vascular disorder, known to usually occur unilaterally; however, recent studies have highlighted vascular abnormalities in the...
Coats' disease is an idiopathic retinal vascular disorder, known to usually occur unilaterally; however, recent studies have highlighted vascular abnormalities in the fellow unaffected eyes. This retrospective study investigated the peripheral vascular features and macular vascular structure of unaffected fellow eyes in patients with unilateral Coats' disease using multimodal imaging tools. We analysed images of patients, including bilateral ultra-widefield imaging, fluorescein angiography (FA), ultra-widefield FA, or standard fundus photography. Available bilateral optical coherence tomography angiography (OCT-A) images were used for macular vascular structure analysis. OCT-A parameters, including foveal avascular zone (FAZ), perfusion index, and vessel density (VD) in the superficial and deep capillary plexuses (SCP, DCP), were calculated using Image J software. The mean age at diagnosis was 34.5 ± 17.9 years. The mean final best-corrected visual acuity of the affected eyes was logMAR 0.78 ± 0.79, while that of the fellow eyes was logMAR 0.04 ± 0.12. Ten fellow eyes had microaneurysms (47.6%), two had tortuous vessel abnormalities (9.5%), and 11(52.4%) had abnormal vascular findings on FA. Although there was a trend towards larger DCP FAZ (1.201 ± 0.086 vs. 1.072 ± 0.226), and lower DCP VD (8.593 ± 1.583 vs. 10.827 ± 3.392) in the affected eyes as measured by the Cirrus machine, the difference was not statistically significant between affected and fellow eyes when measured using the Zeiss Cirrus machine (P = 0.686, P = 0.343, respectively). However, when measured with the Spectralis machine, DCP FAZ was larger in affected eyes (0.828 ± 0.426 vs. 0.254 ± 0.092, P = 0.002) and DCP VD was lower in affected eyes (6.901 ± 2.634 vs. 17.451 ± 7.207, P = 0.002) compared to the fellow eyes, while other parameters showed no significant variations. These findings indicate that there may be subtle vascular abnormalities primarily located in the peripheral regions of the unaffected fellow eyes in patients with unilateral Coats' disease, while the macular microvasculature remains unaffected.
Topics: Humans; Adolescent; Young Adult; Adult; Middle Aged; Retinal Telangiectasis; Retrospective Studies; Tomography, Optical Coherence; Fluorescein Angiography; Macula Lutea
PubMed: 37938623
DOI: 10.1038/s41598-023-45838-1 -
Journal of Diabetes Oct 2023Type 2 diabetes (T2D) is a chronic disease that negatively affects vascular health. A careful assessment of chronic complications, including microcirculation, is...
BACKGROUND
Type 2 diabetes (T2D) is a chronic disease that negatively affects vascular health. A careful assessment of chronic complications, including microcirculation, is mandatory. The computerized nailfold video-capillaroscopy (CNVC) accurately examines the nailfold microvasculature, but its suitability in T2D is currently under investigation.
AIMS
To describe nailfold microvasculature in T2D patients regarding the level of glucose control and chronic microvascular and macrovascular complications.
METHODS
This is a cross-sectional study on 102 consecutive and unselected outpatients with T2D who had undergone CNVC examination. The examination was carried out by using an electronic video-capillaroscope with 300x magnification. Capillaroscopic appearance and capillary changes were described according to well-established parameters. Capillaroscopic parameters were compared between patients with poor glucose control (HbA1c ≥7%) and those with better glucose control (HbA1c <7%) and between patients with chronic complications and those without. Chronic complications were deduced from the anamnestic, laboratory, and instrumental data and the five-item International Index of Erectile Function (IIEF-5) questionnaire.
RESULTS
Nailfold capillaries in patients with HbA1c ≥7% were thicker (p = .019) and longer (p = .021) than in those with better glucose control. Ectasias (p = .017) and microaneurysms (p = .045) were more frequently observed in patients with HbA1c ≥7.0% than those with HbA1c <7.0%. Patients with ED, compared to those without, had a lower frequency of bizarre-shaped capillaries (p = .02). Microaneurysms (p = .02) were more frequently described in patients with carotid stenosis (>20%) than those without.
CONCLUSION
Relevant nailfold microvascular alterations were observed in T2D, most of which were associated with poor glycemic control, ED, and carotid stenosis. Further investigation is needed to recognize the role of CNVC in predicting the onset and evolution of chronic complications and monitoring the effectiveness of antihyperglycemic treatments on microcirculation.
PubMed: 37424059
DOI: 10.1111/1753-0407.13442 -
Journal of Neurology May 2024Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a... (Review)
Review
BACKGROUND
Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk.
METHODS
A biomedical literature search was performed to identify prospective studies that assess the role of retinal imaging derived biomarkers as indicators of stroke risk.
RESULTS
Twenty-four studies were included in this systematic review. The available evidence suggests that wider retinal venules, lower fractal dimension, increased arteriolar tortuosity, presence of retinopathy, and presence of retinal emboli are associated with increased likelihood of stroke. There is weaker evidence to suggest that narrower arterioles and the presence of individual retinopathy traits such as microaneurysms and arteriovenous nicking indicate increased stroke risk. Our review identified three models utilizing artificial intelligence algorithms for the analysis of retinal images to predict stroke. Two of these focused on fundus photographs, whilst one also utilized optical coherence tomography (OCT) technology images. The constructed models performed similarly to conventional risk scores but did not significantly exceed their performance. Only two studies identified in this review used OCT imaging, despite the higher dimensionality of this data.
CONCLUSION
Whilst there is strong evidence that retinal imaging features can be used to indicate stroke risk, there is currently no predictive model which significantly outperforms conventional risk scores. To develop clinically useful tools, future research should focus on utilization of deep learning algorithms, validation in external cohorts, and analysis of OCT images.
Topics: Humans; Stroke; Tomography, Optical Coherence; Retinal Diseases; Retinal Vessels; Risk Assessment; Retina
PubMed: 38430271
DOI: 10.1007/s00415-023-12171-6 -
Microscopy Research and Technique Jan 2024Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World...
Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy-based model into the Fuzzy C-Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M-FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. RESEARCH HIGHLIGHTS: A novel approach is proposed in the article, integrating a neural network energy-based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M-FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.
Topics: Humans; Diabetic Retinopathy; Image Interpretation, Computer-Assisted; Algorithms; Retina; Cluster Analysis; Diabetes Mellitus
PubMed: 37681440
DOI: 10.1002/jemt.24413 -
Heliyon Sep 2023One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To prevent blindness, early detection of DR is therefore necessary. In this paper,...
One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To prevent blindness, early detection of DR is therefore necessary. In this paper, a hybrid model is proposed for diagnosing DR from fundus images. A combination of morphological image processing and Inception v3 deep learning techniques are exploited to detect DR as well as to classify healthy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The proposed algorithm was carried out in several steps such as segmentation of blood vessels, localization and removal of optic disc, and macula, abnormal features detection (microaneurysms, hemorrhages, and neovascularization), and classification. Microaneurysms and hemorrhages that appear in the retina are the early signs of DR. In this work, we have detected microaneurysms and hemorrhages by applying dynamic contrast limited adaptive histogram equalization and threshold value on overlapping patched images. An overall accuracy of 96.83% is obtained to classify DR into five different stages. The better performance demonstrates the effectiveness and novelty of the proposed work as compared to the recent reported work.
PubMed: 37809795
DOI: 10.1016/j.heliyon.2023.e19625