Effects of iron deficiency anemia on peripapillary and macular vessel density determined using optical coherence tomography angiography on children

Korkmaz, Muhammet Furkan; Can, Mehmet Erol; Kazancı, Elif Güler

doi:10.1007/s00417-020-04633-8

Cited by 17 publications

(9 citation statements)

References 30 publications

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“…The relationship between anemia and retinal blood vessels could be explained for several reasons: 1) in severe anemia, retinopathy might develop because of the venous endothelial incompetence and decreased erythrocyte deformability secondary to hypoxia ( Kirkham et al, 1971 ); 2) Choroidal thickness, which is an important indicator of ocular blood circulation, was significantly thinner in IDA patients than those of healthy subjects ( Simsek et al, 2016 ). 3) IDA patients seemed to have significantly lower retinal capillary plexus density ( Korkmaz et al, 2020 ).…”

Section: Discussionmentioning

confidence: 97%

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Zhao

Meng

Su³

et al. 2022

Front. Cell Dev. Biol.

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Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images.Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model.Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81–0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92–0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging.Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.

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Section: Discussionmentioning

confidence: 97%

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Zhao

Meng

Su³

et al. 2022

Front. Cell Dev. Biol.

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“…Recent OCT/OCTA studies discovered that children with iron deficiency anemia (IDA) had significantly lower retinal vessel density and lighter OCT reflectance of the vessel 77 . Hence, an OCT study proposed an ML algorithm extracting texture features from OCT images to classify anemia 78 .…”

Section: Discussionmentioning

confidence: 99%

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Peng

Tseng

Tham

et al. 2022

Asia-Pacific Journal of Ophthalmology

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Purpose: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image–based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening. Methods: A systematic search was conducted on September 1, 2021, using 3 databases—PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used. Results: Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases. Conclusions: Ocular image – based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.

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“…Retinal blood vessels are routinely assessed when diagnosing systemic diseases that can include diabetes, hypertension, anemia, and heart failure; this eye examination reveals a relationship between systemic blood vessels and retinal microvasculature that has proven highly valuable for diagnosing a host of medical conditions [ 11 , 14 , 16 , 29 , 30 , 31 , 32 ]. Medical researchers have long been interested in the correlation between the condition of the retinal blood vessels, their caliber and size, and risk for cardiovascular diseases, including assessment of the risk of cardiac death [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…With the development of new technologies and research methods, the authors began to analyze the condition of retinal microcirculation using OCTA, based on parameters such as VD [ 16 , 30 , 31 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Chronic Heart Failure on Retinal Vessel Density Assessed by Optical Coherence Tomography Angiography in Children with Dilated Cardiomyopathy

Rakusiewicz

Kanigowska

Hautz

et al. 2021

JCM

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(1) Introduction: The aim of this study is to assess retinal vessel density (VD) in the superficial capillary plexus layer (SP) and deep capillary plexus layer (DP) in children with chronic heart failure (CHF) in the course of dilated cardiomyopathy (DCM) using optical coherence tomography angiography (OCTA). (2) Methods: Thirty children with CHF due to DCM lasting more than six months, with an enlarged left ventricle and impaired left ventricular systolic function (left ventricular ejection fraction (LVEF) ≤ 55%), were enrolled to have both their eyes assessed for this study. Mean age of the children was 9.9 ± 3.57 years. The control group consisted of an additional 30 children without CHF (mean age 11.27 ± 3.33 years) matched for age and gender against the study group. All participants underwent transthoracic echocardiography to measure LVEF using Simpson method. Blood serum was tested for N-terminal-pro-brain natriuretic peptide (NT-proBNP) marker value. All children underwent OCTA with evaluation of the foveal avascular zone (FAZ), whole superficial vessel density (wsVD), foveal superficial vessel density (fsVD), parafoveal superficial vessel density (psVD), whole deep vessel density (wdVD), foveal deep vessel density (fdVD), parafoveal deep vessel density (pdVD), whole thickness (WT), foveal thickness (FT), and parafoveal thickness (PFT). (3) Results: Retinal VD in SP was significantly lower in children with CHF as compared to the controls. The following SP parameters in the study group were statistically significantly lower than these same measurements for the control group. Details, with study group findings quantified first, include wsVD (46.2% vs. 49.83%, p < 0.05), fsVD (18.07% vs. 24.15%, p < 0.05), and psVD (49.24% vs. 52.51%, p < 0.05). The WT (311.03 micrometers (μm) vs. 323.55 μm, p < 0.05), FT (244.57 μm vs. 256.98 μm, p < 0.05), and PFT (320.63 μm vs. 332.02 μm, p < 0.05). No significant differences in DP retinal VD were found between the two groups. No statistically significant differences in the FAZ were found. The fsVD and FT were correlated with biometry and the age of the study participants. There was a correlation between FAZ and FT (p < 0.001). There were no correlations between retinal VD in both plexuses and refractive error, sex, NT-proBNP, and LVEF. (4) Conclusions: In children with CHF in the course of DCM as compared to the control group, significantly decreased retinal VD in SP was observed. The results of our study indicate that measurements of the OCTA may be a useful diagnostic method in children with chronic heart failure, but it is necessary to conduct further studies in larger groups of participants and long-term observation of these patients.

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Effects of iron deficiency anemia on peripapillary and macular vessel density determined using optical coherence tomography angiography on children

Cited by 17 publications

References 30 publications

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

The Impact of Chronic Heart Failure on Retinal Vessel Density Assessed by Optical Coherence Tomography Angiography in Children with Dilated Cardiomyopathy

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