A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts’ evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts’ prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts’ predication in full retinal vascular network evaluation.

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Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Nature Communications ◽

10.1038/s41467-021-25138-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ling-Ping Cen ◽

Jie Ji ◽

Jian-Wei Lin ◽

Si-Tong Ju ◽

Hong-Jie Lin ◽

...

Keyword(s):

Deep Learning ◽

High Efficiency ◽

Characteristic Curve ◽

Weighted Average ◽

Age Related Macular Degeneration ◽

Learning Platform ◽

Age Related ◽

Public Data ◽

Primary Test ◽

Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

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Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

Data Science and Engineering ◽

10.1007/s41019-021-00167-z ◽

2021 ◽

Author(s):

Rubina Sarki ◽

Khandakar Ahmed ◽

Hua Wang ◽

Yanchun Zhang ◽

Jiangang Ma ◽

...

Keyword(s):

Image Processing ◽

Image Quality ◽

Geometric Transformation ◽

Diabetic Patients ◽

Fundus Image ◽

Classification Problems ◽

Specificity And Sensitivity ◽

Retinal Fundus Image ◽

Retinal Fundus ◽

Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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Oxidative Stress Mediates Vascular Tortuosity

Antioxidants ◽

10.3390/antiox10060926 ◽

2021 ◽

Vol 10 (6) ◽

pp. 926

Author(s):

Toshio Fumoto ◽

Shouhei Kinoshita ◽

Takao Sasaki ◽

Norihito Shimamura ◽

Hiroki Ohkuma

Keyword(s):

Oxidative Stress ◽

Basilar Artery ◽

Morphological Changes ◽

Imaging Techniques ◽

Pcr Analysis ◽

Adult Rats ◽

Vascular Tortuosity ◽

Bilateral Common Carotid Artery ◽

Stress Related Genes ◽

Underlying Mechanisms

Vascular tortuosity is associated with various disorders and is being increasingly detected through advances in imaging techniques. The underlying mechanisms for vascular tortuosity, however, remain unclear. Here, we tested the hypothesis that oxidative stress mediates the generation of tortuous vessels. We used the bilateral common carotid artery (CCA) ligation model to induce vascular tortuosity. Both young and adult rats showed basilar artery tortuous morphological changes one month after bilateral CCA ligation. These tortuous changes were permanent but more pronounced in the adult rats. Microarray and real-time PCR analysis revealed that these tortuous changes were accompanied by the induction of oxidative stress-related genes. Moreover, the indicated model in rabbits showed that tortuous morphological changes to the basilar artery were suppressed by antioxidant treatment. These results are highly suggestive of the significance of oxidative stress in the development of vascular tortuosity. Although further studies will be needed to elucidate the possible mechanisms by which oxidative stress enhances vascular tortuosity, our study also points toward possible prophylaxis and treatment for vascular tortuosity.

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Berberine and Ginsenoside Rb1 Ameliorate Depression-Like Behavior in Diabetic Rats

The American Journal of Chinese Medicine ◽

10.1142/s0192415x21500579 ◽

2021 ◽

pp. 1-19

Author(s):

Jing-Hua Zhang ◽

Hui-Zeng Yang ◽

Hao Su ◽

Jun Song ◽

Yu Bai ◽

...

Keyword(s):

Insulin Resistance ◽

Glucose Metabolism ◽

Plasma Cortisol ◽

Adrenocorticotropic Hormone ◽

Morphological Changes ◽

Diabetic Rats ◽

Diabetic Patients ◽

Mild Stress ◽

Ginsenoside Rb1 ◽

Nissl Staining

Rhizoma coptidis(Huang-lian) and Asian ginseng have been widely used in the treatment of diabetes and other concurrent diseases with apparent effects. This study investigated the effects of the active ingredients of R. coptidis and ginseng, berberine and ginsenoside Rb1, on depression-like behavior in a rat diabetes model. The animal model was established via a high-fat diet and intraperitoneal injection of streptozotocin, while the animal’s depression-like behavior was induced via chronic unpredictable mild stress. These experimental rats were divided into four groups: control, depression-like behavior (DLB), metformin plus fluoxetine hydrochloride (M+FH), and berberine plus ginsenoside Rb1 (B+GRb1) groups. Glucose metabolism and insulin resistance were evaluated by oral glucose test and glucose clamp study. Depression-like behavior was evaluated via behavioral analyses, including forced swim, sucrose preference, elevated plus maze, and open-field tests. HE and Nissl staining, plasma cortisol expression of adrenocorticotropic hormone, and brain-derived neurotrophic factor (BDNF) levels were assayed to explore the mechanisms of action. Compared with the control, rats in the DLB group had a significant increase in the levels of blood glucose and depression-like behavior. The B+GRb1 group significantly improved glucose metabolism and insulin resistance, reduced depression-like behavior, downregulated levels of plasma cortisol and adrenocorticotropic hormone under stress, and upregulated BDNF protein expression compared to the DLB rats. HE and Nissl staining data revealed that B+GRb1 protected neurons from pathological and morphological changes. Thus, berberine and ginsenoside Rb1 not only improved glucose metabolism in diabetic rats but also ameliorated their depression-like behavior under chronic unpredictable stress. Mechanistically, studied data with plasma hormonal levels and brain neuronal pathological/morphological changes supported the observed effects. The combination of berberine and ginsenoside Rb1 may have a clinical value in the management of diabetic patients with depression.

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Retinal Vascular Tortuosity and Recurrent Retinal Haemorrhages in a Patient with Non-Chromaffin Paraganglioma

Ophthalmologica ◽

10.1159/000309541 ◽

1985 ◽

Vol 191 (1) ◽

pp. 57-60

Author(s):

Erling Grønvold Olsen ◽

Amund Ringvold ◽

Sigvald B. Refsum

Keyword(s):

Vascular Tortuosity ◽

Retinal Vascular Tortuosity ◽

Retinal Haemorrhages

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Retinal Blood Vessel and Optic Disc Segmentation

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1963.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 833-838

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessel ◽

Optic Disc ◽

Diabetic Patients ◽

Retinal Vessels ◽

Retinal Blood Vessel ◽

Computerized Screening ◽

Retinal Fundus

Diabetic Retinopathy (DR) is a main source of vision misfortune in diabetic patients. DR is a predominantly caused because of the harm caused in retinal veins of a diabetic patients. It is fundamental to recognize and fragment their tinal veins for DR identification and determination, which avoids prior vision misfortune in diabetic patients. The PC helped programmed discovery and division of veins through the end of optic location district in Retina. Optic Disc (OD) discovery is a principle step while creating computerized screening framework for diabetic retinopathy. This is a technique to naturally recognize the situation of the OD in advanced retinal fundus pictures. The strategy begins by normalizing glow and difference all through the picture utilizing brightening evening out and versatile histogram balance techniques individually. The OD recognition calculation depends on coordinating the normal directional example of the retinal veins. Henceforth, a straightforward coordinated channel is proposed to generally coordinate the headings of the vessels at the OD region. The retinal vessels are portioned utilizing a basic and standard 2-D Gaussian coordinated channel.

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Dynamics of the Tracheal Airway and Its Influences on Respiratory Airflows: An Exemplar Study

Journal of Biomechanical Engineering ◽

10.1115/1.4043723 ◽

2019 ◽

Vol 141 (11) ◽

Cited By ~ 1

Author(s):

Bora Sul ◽

Talissa Altes ◽

Kai Ruppert ◽

Kun Qing ◽

Daniel S. Hariprasad ◽

...

Keyword(s):

Morphological Changes ◽

Minute Ventilation ◽

Correlation Coefficients ◽

Dynamic Imaging ◽

Imaging Data ◽

Cross Sectional ◽

Mild Conditions ◽

Large Airways ◽

Magnetic Resonance Imaging Mri

Respiration is a dynamic process accompanied by morphological changes in the airways. Although deformation of large airways is expected to exacerbate pulmonary disease symptoms by obstructing airflow during increased minute ventilation, its quantitative effects on airflow characteristics remain unclear. Here, we used in vivo dynamic imaging and examined the effects of tracheal deformation on airflow characteristics under different conditions based on imaging data from a single healthy volunteer. First, we measured tracheal deformation profiles of a healthy lung using magnetic resonance imaging (MRI) during forced exhalation, which we simulated to characterize the subject-specific airflow patterns. Subsequently, for both inhalation and exhalation, we compared the airflows when the modeled deformation in tracheal cross-sectional area was 0% (rigid), 33% (mild), 50% (moderate), or 75% (severe). We quantified differences in airflow patterns between deformable and rigid airways by computing the correlation coefficients (R) and the root-mean-square of differences (Drms) between their velocity contours. For both inhalation and exhalation, airflow patterns were similar in all branches between the rigid and mild conditions (R > 0.9; Drms < 32%). However, airflow characteristics in the moderate and severe conditions differed markedly from those in the rigid and mild conditions in all lung branches, particularly for inhalation (moderate: R > 0.1, Drms < 76%; severe: R > 0.2, Drms < 96%). Our exemplar study supports the use of a rigid airway assumption to compute flows for mild deformation. For moderate or severe deformation, however, dynamic contraction should be considered, especially during inhalation, to accurately predict airflow and elucidate the underlying pulmonary pathology.

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Assessing Diabetic Foot Infections With the ThermoScale: A Comparative Thermometry Device Designed as a Patient Self-Screening Tool

The International Journal of Lower Extremity Wounds ◽

10.1177/1534734620948304 ◽

2020 ◽

pp. 153473462094830

Author(s):

Laure Arts ◽

Johan De Neve ◽

Samira Baharlou ◽

Nathalie Denecker ◽

Laura Kerselaers ◽

...

Keyword(s):

Temperature Measurement ◽

Diabetic Foot ◽

Characteristic Curve ◽

Screening Method ◽

Area Under The Curve ◽

Control Group ◽

Measurement Technology ◽

Diabetic Patients ◽

Home Setting ◽

Diabetic Foot Infections

Diabetic foot infection (DFI) is an important risk factor for amputation, and late diagnosis or referral is often incriminated for poor outcome. To enable an earlier diagnosis of DFI, comparative foot thermometry has been suggested as a self-screening method for patients in a home setting. We validated the efficacy of the ThermoScale, a weighing scale outfitted with temperature sensors that allows accurate temperature measurement in both feet. Temperature differentials in DFI patients (n = 52) were compared with a control group of similar diabetic patients (n = 45) without any foot wounds. Based on these findings, we drafted a receiver operating characteristic curve to determine an area-under-the-curve of 0.8455. This value suggests that the ThermoScale, as a diagnostic test, is reasonably accurate. A cutoff value of 2.15 °C temperature difference corresponded with a sensitivity of 88.9% and a specificity of 61.5%. As wearables, portable health electronics, and telemedicine become increasingly popular, we think that comparative temperature measurement technology is valuable in improving early diagnosis of DFIs.

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