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L. István Department of Ophthalmology, Faculty of General Medicine, Semmelweis University, Budapest, Hungary

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Z. Z. Nagy Department of Ophthalmology, Faculty of General Medicine, Semmelweis University, Budapest, Hungary
Department of Clinical Ophthalmology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary

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I. Kovács Department of Ophthalmology, Faculty of General Medicine, Semmelweis University, Budapest, Hungary
Department of Clinical Ophthalmology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
Department of Ophthalmology, Weill Cornell Medical College, New York, USA

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Abstract

Purpose

The aim of this study was to assess longitudinal changes in retinal vessel density in diabetic patients using optical coherence tomography angiography (OCTA) to identify the most sensitive parameter for detecting retinopathy progression.

Methods

Patients with diabetes mellitus were enrolled in this study. Each study subject underwent two imaging sessions, during which three OCTA images of the macular area and three images of the optic nerve head were obtained. The two sessions took place one year apart. The OCTA imaging was performed using an AngioVue device. Superficial vessel density was evaluated in the central 3 mm and parafoveal area, and the nonflow area was measured using the built-in automated AngioAnalytics software of the Optovue system.

Results

This study included 78 eyes of 39 diabetic patients (age: 55.16 ± 13.73 years) with a mean of 7.70 ± 1.07 mmol L−1 HgA1c level at baseline. At the one-year visit, the eyes of the diabetic subjects had significantly lower superficial vessel density in the parafoveal macula compared to corresponding values at baseline (p < 0.05). There was no statistically significant difference between the baseline and one-year results for the other vascular density parameters and the foveal avascular zone (p > 0.05).

Conclusion

At the one-year follow-up, we found that vessel density had decreased in the parafoveal ring in these patients, while there was no significant change in other vascular parameters. This result suggests that superficial parafoveal capillary density is the most sensitive OCTA parameter that can be used as a biomarker for diabetic retinopathy progression. None of the other vascular density parameters nor the foveal avascular zone were able to indicate the subtle changes in retinal microcirculation due to the progression of diabetic microvasculopathy.

Abstract

Purpose

The aim of this study was to assess longitudinal changes in retinal vessel density in diabetic patients using optical coherence tomography angiography (OCTA) to identify the most sensitive parameter for detecting retinopathy progression.

Methods

Patients with diabetes mellitus were enrolled in this study. Each study subject underwent two imaging sessions, during which three OCTA images of the macular area and three images of the optic nerve head were obtained. The two sessions took place one year apart. The OCTA imaging was performed using an AngioVue device. Superficial vessel density was evaluated in the central 3 mm and parafoveal area, and the nonflow area was measured using the built-in automated AngioAnalytics software of the Optovue system.

Results

This study included 78 eyes of 39 diabetic patients (age: 55.16 ± 13.73 years) with a mean of 7.70 ± 1.07 mmol L−1 HgA1c level at baseline. At the one-year visit, the eyes of the diabetic subjects had significantly lower superficial vessel density in the parafoveal macula compared to corresponding values at baseline (p < 0.05). There was no statistically significant difference between the baseline and one-year results for the other vascular density parameters and the foveal avascular zone (p > 0.05).

Conclusion

At the one-year follow-up, we found that vessel density had decreased in the parafoveal ring in these patients, while there was no significant change in other vascular parameters. This result suggests that superficial parafoveal capillary density is the most sensitive OCTA parameter that can be used as a biomarker for diabetic retinopathy progression. None of the other vascular density parameters nor the foveal avascular zone were able to indicate the subtle changes in retinal microcirculation due to the progression of diabetic microvasculopathy.

Introduction

Diabetes leads to numerous microvascular complications. Among these, diabetic retinopathy (DR) affects 80% of patients after 15 years of disease duration [1, 2]. Life quality is significantly affected by DR, particularly at the more advanced stages, such as proliferative DR and clinically significant macular oedema, which are both vision-threatening conditions [3]. A recent study found that the risk of developing DR increases by 6% per year [4]. Microvascular damage leads to capillary nonperfusion and ischemia, which upregulates the production of vascular endothelial growth factor (VEGF), resulting in pathologic neovascularisation and increased vascular permeability [5].

Ocular imaging has long played a significant role in the diagnosis and management of DR. Traditionally, the gold standard procedure for visualising retinal vascular perfusion and assessing the changes in early DR was fluorescein angiography. Optical coherence tomography angiography (OCTA) is an innovative method for visualising and analysing the retinal and choroidal vasculature without the use of an intravenous dye. It is a fast and non-invasive imaging technique that detects retinal blood flow based on motion contrast technology. Using OCTA technology, it is possible to visualise the different retinal and choroidal vascular layers separately and to study the retinal microcirculation both qualitatively and quantitatively [6] with a high level of reproducibility and reliability [7]. It makes possible the quantitative measurement of blood flow in the macular area and the optic nerve head, and the accurate visualisation of microvascular abnormalities and capillary dropout areas in retinal vascular diseases. The built-in AngioAnalytics software released by the AngioVue OCTA system allows the quantification of the flow area, nonflow area, and flow density around the fovea. Since OCTA is able to indicate subtle changes in the microvasculature even before clinical vascular abnormalities are observed, it can be considered a useful tool in identifying diabetic retinal abnormalities in the early stages. Numerous studies have reported changes in the retinal microvasculature in diabetic patients, including the enlargement and distortion of the foveal avascular zone (FAZ), decreases in capillary vessel density (VD), and branching complexity (fractal dimension) [8–17].

The aim of the present study was to assess longitudinal changes in retinal VD in diabetic patients using OCTA to identify the most sensitive parameter for detecting retinopathy progression.

Methods

Subjects with Type 1 and Type 2 diabetes were recruited from the outpatient clinic of the Department of Ophthalmology at Semmelweis University. A total of 78 eyes of 39 patients with diabetes mellitus were included in this study. Exclusion criteria were any history of intraocular surgery; associated ocular disease such as age-related macular degeneration, glaucoma, and vitreomacular disease; previous intraocular anti–vascular endothelial growth factor, steroid, or laser treatment; the presence of clinically significant lens opacities; or a refractive error higher than 6 dioptres. All patients underwent a comprehensive ophthalmic examination, including the measurement of visual acuity and slit lamp and fundus examinations. Each study subject underwent two imaging sessions, during which three OCTA images of the macular area and three images of the optic nerve head were obtained. The two sessions took place one year apart. The OCTA imaging was performed by one trained examiner using the AngioVue device with a split-spectrum amplitude decorrelation angiography (SSADA) software algorithm (RTVue XR Avanti with AngioVue, Optovue Inc, Fremont, California, USA). The device obtains 70,000 A-scans per second for approximately 3.0 s. Images with movement artefacts (such as white line artefacts, vessel doubling, vessel discontinuities, or noise), projection artefacts and segmentation errors were excluded. Scan quality (SQ) was required to be above SQ 5: Images with an SQ of 5 or below were excluded.

Superficial VD was evaluated in the central 3 mm and parafoveal area, and the nonflow area was measured using the built-in automated AngioAnalytics software of the Optovue system. The fovea was defined as the area within the central 1 mm ring of the Early Treatment Diabetic Retinopathy Study grid. The parafoveal ring was considered as an annulus surrounding the fovea with an outer diameter of 3 mm and an inner diameter of 1 mm. The nonflow area corresponded to the FAZ and was measured in square millimetres (mm2) using the automatic nonflow tool of the software. Full retinal thickness was measured between the internal limiting membrane and the retinal pigment epithelium in the central 1 mm area. Statistical analysis was performed using SPSS software (version 23.0, IBM, Armonk, NY, USA).

Results

The study included 78 eyes of 39 diabetic patients (age: 55.16 ± 13.73 years), with a mean of 7.70 ± 1.07 mmol L−1 HgA1c level at baseline. Values for SQ ranged from 5 to 10; the overall mean SQ was 6.67 ± 1.75 at baseline and 7.11 ± 1.37 at the one-year visit, and the difference proved to be statistically non-significant (p > 0.05). At baseline, SQ values showed significant positive correlation with superficial retinal capillary VD values (Fig. 1). However, as an indicator of changes in retinal blood flow, fluctuation in OCTA metrics was observed in images taken from the same subject at baseline and after one year, as can be seen in Fig. 2. At the one-year visit, the eyes of the diabetic subjects had significantly lower superficial VD in the parafoveal macula compared to the corresponding values at baseline (Table 1; p < 0.05). There was no statistically significant difference between the baseline and one-year results for the other vascular density parameters or FAZ (Table 1; p > 0.05).

Fig. 1.
Fig. 1.

The correlation between scan quality and superficial retinal capillary density at baseline

Citation: Developments in Health Sciences 5, 1; 10.1556/2066.2023.00049

Fig. 2.
Fig. 2.

Representative OCTA image of a diabetic patient, demonstrating a decrease in superficial retinal capillary density at one year (right) compared to baseline

Citation: Developments in Health Sciences 5, 1; 10.1556/2066.2023.00049

Table 1.

Changes in OCTA parameters from baseline to one-year visit

BaselineOne yearp
CRT (ųm)267.21 ± 40.71266.63 ± 44.840.82
sVDWI (%)41.48 ± 4.8141.09 ± 6.220.57
sVDPF (%)43.68 ± 5.2542.28 ± 5.750.02
dVDWI (%)44.59 ± 5.2944.95 ± 5.350.63
dVDPF (%)46.68 ± 5.8347.21 ± 5.850.49
FAZ (mm2)0.30 ± 0.130.32 ± 0.160.07
SQ (unit)6.76 ± 1.757.11 ± 1.370.24

Notes. CRT: central retinal thickness, sVDWI: superficial vessel density whole image, sVDPF: superficial vessel density para foveal, dVDWI: deep vessel density whole image, dVDPF: deep vessel density para foveal, FAZ: foveal avascular zone, SQ: scan quality

Discussion

In the present study we analysed qualitative and quantitative changes in retinal circulation in patients with diabetes mellitus. At the one-year follow-up we found that VD had decreased in the parafoveal ring in these patients, while there was no significant change in other vascular parameters. This result suggests that superficial parafoveal capillary density is the most sensitive OCTA parameter that can be used as a biomarker for DR progression. None of the other VD parameters nor FAZ were able to indicate the subtle changes in retinal microcirculation due to the progression of diabetic microvasculopathy.

The incidence of diabetes is significantly increasing worldwide. Due to the various possible complications, it places a significant burden on health care. The small vessel damage that occurs in diabetes leads to capillary loss and ischemia and increases VEGF production, which then leads to abnormal new blood vessel formation and increased vascular permeability. Diabetic maculopathy – which can develop at any stage of DR – accounts for 4.8% of blindness worldwide [18]. The correlation between inadequate glycaemic control and the development and progression of DR has long been known [19]. Among diabetic patients, it is highly important to recognise those patients at increased risk of developing potentially vision-threatening complications. Ocular imaging techniques have long played a significant role in the screening and follow-up of DR. However, until recently, fluorescein angiography was considered the gold standard.

The use of OCTA, a new, non-invasive and easily repeatable imaging technique, makes it possible to detect early signs of retinal microvascular abnormalities. The imaging method is suitable for the follow-up of conditions affecting the retinal blood flow. The most important limiting factors for OCTA are related to image quality. Based on the signal intensity of the acquired image, the software calculates a signal quality score for every scan. Several factors affect the scan quality, such as blink artefacts, ocular saccades, media opacities and OCTA operator skills. The Scan Quality index is a unitless parameter on a scale of 1–10 (the larger the better), which is automatically calculated for each scan by the RTVue-XR AngioVue software. Previous studies have highlighted the accuracy of OCTA measurements in detecting subtle changes in retinal blood flow in diabetes. Image quality has a significant effect on OCTA measurement scores [20–25] and must be controlled when assessing changes in retinal blood flow. In this study, we also analysed the correlation between scan quality and the measured values of the OCTA parameters to evaluate whether image quality significantly influenced the OCTA measurements. Confirming previous results [20, 21], the significant SQ–VD correlation, even in high-quality images, indicates that the compensation of SQ in DR progression analysis is recommended for the accurate comparison of scans at follow-up in these patients.

Conclusion

According to the results of the present study, superficial parafoveal capillary density is the most sensitive parameter for detecting subtle changes in retinal microcirculation, and might serve as a biomarker when assessing DR progression. However, further studies are recommended, since the relationship would be better described by the analysis of longitudinal data in a larger cohort.

Authors' contribution

Conceptualization: IK, ZZN, Investigation: LI, writing of the manuscript: IK, LI.

Ethical approval

In this prospective observational cross-sectional study, subjects with Type 1 and Type 2 diabetes were recruited from the outpatient clinic of the Department of Ophthalmology at Semmelweis University. The study followed the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board for Human Research of Semmelweis University, Budapest.

Conflicts of interest/Funding

The authors declare no conflict of interest and no financial support was received for this study.

Acknowledgements

None.

References

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    Wong TY, Cheung CM, Larsen M, Sharma S, Simó R. Diabetic retinopathy. Nat Rev Dis Primers 2016;2:16012. https://doi.org/10.1038/nrdp.2016.12.

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    Cunha-Vaz J, Ribeiro L, Lobo C. Phenotypes and biomarkers of diabetic retinopathy. Prog Retin Eye Res 2014;41:90111. https://doi.org/10.1016/j.preteyeres.2014.03.003.

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    Shahlaee A, Samara WA, Hsu J, et al. In vivo assessment of macular vascular density in healthy human eyes using optical coherence tomography angiography. Am J Ophthalmol 2016;165:3946. https://doi.org/10.1016/j.ajo.2016.02.018.

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    Li J, Yang YQ, Yang DY, et al. Reproducibility of perfusion parameters of optic disc and macula in rhesus monkeys by optical coherence tomography angiography. Chin Med J (Engl) 2016;129:10871090. https://doi.org/10.4103/0366-6999.180532.

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    de Carlo TE, Romano A, Waheed NK, Duker JS. A review of optical coherence tomography angiography (OCTA). Int J Retina Vitreous 2015;1:5. https://doi.org/10.1186/s40942-015-0005-8.

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    Ishibazawa A, Nagaoka T, Takahashi A, et al. Optical coherence tomography angiography in diabetic retinopathy: a prospective pilot study. Am J Ophthalmol 2015;160:3544.e1. https://doi.org/10.1016/j.ajo.2015.04.021.

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    Hwang TS, Jia Y, Gao SS, et al. Optical coherence tomography angiography features of diabetic retinopathy. Retina 2015;35:23712376. https://doi.org/10.1097/IAE.0000000000000716.

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    Miwa Y, Murakami T, Suzuma K, et al. Relationship between functional and structural changes in diabetic vessels in optical coherence tomography angiography. Sci Rep 2016;6:29064. https://doi.org/10.1038/srep29064.

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    Salz DA, de Carlo TE, Adhi M, et al. Select features of diabetic retinopathy on swept-source optical coherence tomographic angiography compared with fluorescein angiography and normal eyes. JAMA Ophthalmol 2016;134:644650. https://doi.org/10.1001/jamaophthalmol.2016.0600.

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    Freiberg FJ, Pfau M, Wons J, Wirth MA, Becker MD, Michels S. Optical coherence tomography angiography of the foveal avascular zone in diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol 2016;254:10511058. https://doi.org/10.1007/s00417-015-3148-2.

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    Bresnick GH, Condit R, Syrjala S, Palta M, Groo A, Korth K. Abnormalities of the foveal avascular zone in diabetic retinopathy. Arch Ophthalmol 1984;102:12861293. https://doi.org/10.1001/archopht.1984.01040031036019.

    • Search Google Scholar
    • Export Citation
  • 15.

    Arend O, Wolf S, Jung F, et al. Retinal microcirculation in patients with diabetes mellitus: dynamic and morphological analysis of perifoveal capillary network. Br J Ophthalmol 1991;75:514518. https://doi.org/10.1136/bjo.75.9.514.

    • Search Google Scholar
    • Export Citation
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    Di G, Weihong Y, Xiao Z, et al. A morphological study of the foveal avascular zone in patients with diabetes mellitus using optical coherence tomography angiography. Graefes Arch Clin Exp Ophthalmol 2016;254:873879. https://doi.org/10.1007/s00417-015-3143-7.

    • Search Google Scholar
    • Export Citation
  • 17.

    Couturier A, Mané V, Bonnin S, et al. Capillary plexus anomalies in diabetic retinopathy on optical coherence tomography angiography. Retina 2015;35:23842391. https://doi.org/10.1097/IAE.0000000000000859.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wong T, Klein K. The epidemiology of eye diseases in diabetes. In: Ekoé JM, Rewers M, Williams R, et al., editors. The epidemiology of diabetes mellitus. 2nd ed. Oxford: John Wiley and Sons; 2008, pp. 475497.

    • Search Google Scholar
    • Export Citation
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    Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet 2010;376:124136.

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    Czakó C, István L, Ecsedy M, et al. The effect of image quality on the reliability of OCT angiography measurements in patients with diabetes. Int J Retina Vitreous 2019;5:46. https://doi.org/10.1186/s40942-019-0197-4.

    • Search Google Scholar
    • Export Citation
  • 21.

    Czakó C, István L, Benyó F, et al. The impact of deterministic signal loss on OCT angiography measurements. Transl Vis Sci Technol 2020;9:10. https://doi.org/10.1167/tvst.9.5.10.

    • Search Google Scholar
    • Export Citation
  • 22.

    Yu JJ, Camino A, Liu L, et al. Signal strength reduction effects in OCT angiography. Ophthalmol Retina 2019;3:835842. https://doi.org/10.1016/j.oret.2019.04.029.

    • Search Google Scholar
    • Export Citation
  • 23.

    Al-Sheikh M, Ghasemi Falavarjani K, Akil H, Sadda SR. Impact of image quality on OCT angiography based quantitative measurements. Int J Retina Vitreous 2017;3:13. https://doi.org/10.1186/s40942-017-0068-9.

    • Search Google Scholar
    • Export Citation
  • 24.

    Yu S, Frueh BE, Steinmair D, et al. Cataract significantly influences quantitative measurements on swept-source optical coherence tomography angiography imaging. PLoS One 2018;13:e0204501. https://doi.org/10.1371/journal.pone.0204501.

    • Search Google Scholar
    • Export Citation
  • 25.

    Holló G. Influence of posterior subcapsular cataract on structural OCT and OCT angiography vessel density measurements in the peripapillary retina. J Glaucoma 2019;28:e61e63. https://doi.org/10.1097/IJG.0000000000001147.

    • Search Google Scholar
    • Export Citation
  • 1.

    Wong TY, Cheung CM, Larsen M, Sharma S, Simó R. Diabetic retinopathy. Nat Rev Dis Primers 2016;2:16012. https://doi.org/10.1038/nrdp.2016.12.

    • Search Google Scholar
    • Export Citation
  • 2.

    Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin epidemiologic study of diabetic retinopathy. III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Arch Ophthalmol 1984;102:527532. https://doi.org/10.1001/archopht.1984.01040030405011.

    • Search Google Scholar
    • Export Citation
  • 3.

    Fenwick EK, Pesudovs K, Rees G, et al. The impact of diabetic retinopathy: understanding the patient's perspective. Br J Ophthalmol 2011;95:774782. https://doi.org/10.1136/bjo.2010.191312.

    • Search Google Scholar
    • Export Citation
  • 4.

    Zhang X, Saaddine JB, Chou CF, et al. Prevalence of diabetic retinopathy in the United States, 2005–2008. JAMA 2010;304:649656. https://doi.org/10.1001/jama.2010.1111.

    • Search Google Scholar
    • Export Citation
  • 5.

    Cunha-Vaz J, Ribeiro L, Lobo C. Phenotypes and biomarkers of diabetic retinopathy. Prog Retin Eye Res 2014;41:90111. https://doi.org/10.1016/j.preteyeres.2014.03.003.

    • Search Google Scholar
    • Export Citation
  • 6.

    Shahlaee A, Samara WA, Hsu J, et al. In vivo assessment of macular vascular density in healthy human eyes using optical coherence tomography angiography. Am J Ophthalmol 2016;165:3946. https://doi.org/10.1016/j.ajo.2016.02.018.

    • Search Google Scholar
    • Export Citation
  • 7.

    Li J, Yang YQ, Yang DY, et al. Reproducibility of perfusion parameters of optic disc and macula in rhesus monkeys by optical coherence tomography angiography. Chin Med J (Engl) 2016;129:10871090. https://doi.org/10.4103/0366-6999.180532.

    • Search Google Scholar
    • Export Citation
  • 8.

    de Carlo TE, Romano A, Waheed NK, Duker JS. A review of optical coherence tomography angiography (OCTA). Int J Retina Vitreous 2015;1:5. https://doi.org/10.1186/s40942-015-0005-8.

    • Search Google Scholar
    • Export Citation
  • 9.

    Ishibazawa A, Nagaoka T, Takahashi A, et al. Optical coherence tomography angiography in diabetic retinopathy: a prospective pilot study. Am J Ophthalmol 2015;160:3544.e1. https://doi.org/10.1016/j.ajo.2015.04.021.

    • Search Google Scholar
    • Export Citation
  • 10.

    Hwang TS, Jia Y, Gao SS, et al. Optical coherence tomography angiography features of diabetic retinopathy. Retina 2015;35:23712376. https://doi.org/10.1097/IAE.0000000000000716.

    • Search Google Scholar
    • Export Citation
  • 11.

    Miwa Y, Murakami T, Suzuma K, et al. Relationship between functional and structural changes in diabetic vessels in optical coherence tomography angiography. Sci Rep 2016;6:29064. https://doi.org/10.1038/srep29064.

    • Search Google Scholar
    • Export Citation
  • 12.

    Salz DA, de Carlo TE, Adhi M, et al. Select features of diabetic retinopathy on swept-source optical coherence tomographic angiography compared with fluorescein angiography and normal eyes. JAMA Ophthalmol 2016;134:644650. https://doi.org/10.1001/jamaophthalmol.2016.0600.

    • Search Google Scholar
    • Export Citation
  • 13.

    Freiberg FJ, Pfau M, Wons J, Wirth MA, Becker MD, Michels S. Optical coherence tomography angiography of the foveal avascular zone in diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol 2016;254:10511058. https://doi.org/10.1007/s00417-015-3148-2.

    • Search Google Scholar
    • Export Citation
  • 14.

    Bresnick GH, Condit R, Syrjala S, Palta M, Groo A, Korth K. Abnormalities of the foveal avascular zone in diabetic retinopathy. Arch Ophthalmol 1984;102:12861293. https://doi.org/10.1001/archopht.1984.01040031036019.

    • Search Google Scholar
    • Export Citation
  • 15.

    Arend O, Wolf S, Jung F, et al. Retinal microcirculation in patients with diabetes mellitus: dynamic and morphological analysis of perifoveal capillary network. Br J Ophthalmol 1991;75:514518. https://doi.org/10.1136/bjo.75.9.514.

    • Search Google Scholar
    • Export Citation
  • 16.

    Di G, Weihong Y, Xiao Z, et al. A morphological study of the foveal avascular zone in patients with diabetes mellitus using optical coherence tomography angiography. Graefes Arch Clin Exp Ophthalmol 2016;254:873879. https://doi.org/10.1007/s00417-015-3143-7.

    • Search Google Scholar
    • Export Citation
  • 17.

    Couturier A, Mané V, Bonnin S, et al. Capillary plexus anomalies in diabetic retinopathy on optical coherence tomography angiography. Retina 2015;35:23842391. https://doi.org/10.1097/IAE.0000000000000859.

    • Search Google Scholar
    • Export Citation
  • 18.

    Wong T, Klein K. The epidemiology of eye diseases in diabetes. In: Ekoé JM, Rewers M, Williams R, et al., editors. The epidemiology of diabetes mellitus. 2nd ed. Oxford: John Wiley and Sons; 2008, pp. 475497.

    • Search Google Scholar
    • Export Citation
  • 19.

    Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet 2010;376:124136.

  • 20.

    Czakó C, István L, Ecsedy M, et al. The effect of image quality on the reliability of OCT angiography measurements in patients with diabetes. Int J Retina Vitreous 2019;5:46. https://doi.org/10.1186/s40942-019-0197-4.

    • Search Google Scholar
    • Export Citation
  • 21.

    Czakó C, István L, Benyó F, et al. The impact of deterministic signal loss on OCT angiography measurements. Transl Vis Sci Technol 2020;9:10. https://doi.org/10.1167/tvst.9.5.10.

    • Search Google Scholar
    • Export Citation
  • 22.

    Yu JJ, Camino A, Liu L, et al. Signal strength reduction effects in OCT angiography. Ophthalmol Retina 2019;3:835842. https://doi.org/10.1016/j.oret.2019.04.029.

    • Search Google Scholar
    • Export Citation
  • 23.

    Al-Sheikh M, Ghasemi Falavarjani K, Akil H, Sadda SR. Impact of image quality on OCT angiography based quantitative measurements. Int J Retina Vitreous 2017;3:13. https://doi.org/10.1186/s40942-017-0068-9.

    • Search Google Scholar
    • Export Citation
  • 24.

    Yu S, Frueh BE, Steinmair D, et al. Cataract significantly influences quantitative measurements on swept-source optical coherence tomography angiography imaging. PLoS One 2018;13:e0204501. https://doi.org/10.1371/journal.pone.0204501.

    • Search Google Scholar
    • Export Citation
  • 25.

    Holló G. Influence of posterior subcapsular cataract on structural OCT and OCT angiography vessel density measurements in the peripapillary retina. J Glaucoma 2019;28:e61e63. https://doi.org/10.1097/IJG.0000000000001147.

    • Search Google Scholar
    • Export Citation
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Senior Editors

Editor-in-Chief: Zoltán Zsolt NAGY
Vice Editors-in-Chief: Gabriella Bednárikné DÖRNYEI, Ákos KOLLER
Managing Editor: Johanna TAKÁCS

Editorial Board

  • Zoltán BALOGH (Department of Nursing, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Klára GADÓ (Department of Clinical Studies, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • István VINGENDER (Department of Social Sciences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Attila DOROS (Department of Imaging and Medical Instrumentation, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Judit Helga FEITH (Department of Social Sciences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Mónika HORVÁTH (Department of Physiotherapy, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Illés KOVÁCS (Department of Clinical Ophthalmology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Ildikó NAGYNÉ BAJI (Department of Applied Psychology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Tamás PÁNDICS (Department for Epidemiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • József RÁCZ (Department of Addictology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Lajos A. RÉTHY (Department of Family Care Methodology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • János RIGÓ (Department of Clinical Studies in Obstetrics and Gynaecology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Andrea SZÉKELY (Department of Oxyology and Emergency Care, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Márta VERESNÉ BÁLINT (Department of Dietetics and Nutritional Sicences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Gyula DOMJÁN (Department of Clinical Studies, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Péter KRAJCSI (Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • György LÉVAY (Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Csaba NYAKAS (Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Vera POLGÁR (Department of Morphology and Physiology, InFaculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • László SZABÓ (Department of Family Care Methodology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Katalin TÁTRAI-NÉMETH (Department of Dietetics and Nutrition Sciences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Katalin KOVÁCS ZÖLDI (Department of Social Sciences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • Gizella ÁNCSÁN (Library, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary)
  • András FALUS (Department of Genetics, Cell- and Immunbiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary)
  • Zoltán UNGVÁRI (Department of Public Health, Faculty of medicine, Semmelweis University, Budapest, Hungary)
  • Romána ZELKÓ (Faculty of Pharmacy, Semmelweis University, Budapest, Hungary)
  • Mária BARNAI (Faculty of Health Sciences and Social Studies, University of Szeged, Szeged, Hungary)
  • László Péter KANIZSAI (Department of Emergency Medicine, Medical School, University of Pécs, Pécs, Hungary)
  • Bettina FŰZNÉ PIKÓ (Department of Behavioral Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary)
  • Imre SEMSEI (Faculty of Health, University of Debrecen, Debrecen, Hungary)
  • Teija-Kaisa AHOLAAKKO (Laurea Universities of Applied Sciences, Vantaa, Finland)
  • Ornella CORAZZA (University of Hertfordshire, Hatfield, Hertfordshire, United Kingdom)
  • Oliver FINDL (Department of Ophthalmology, Hanusch Hospital, Vienna, Austria)
  • Tamás HACKI (University Hospital Regensburg, Phoniatrics and Pediatric Audiology, Regensburg, Germany)
  • Xu JIANGUANG (Shanghai University of Traditional Chinese Medicine, Shanghai, China)
  • Paul GM LUITEN (Department of Molecular Neurobiology, University of Groningen, Groningen, Netherlands)
  • Marie O'TOOLE (Rutgers School of Nursing, Camden, United States)
  • Evridiki PAPASTAVROU (School of Health Sciences, Cyprus University of Technology, Lemesos, Cyprus)
  • Pedro PARREIRA (The Nursing School of Coimbra, Coimbra, Portugal)
  • Jennifer LEWIS SMITH (Collage of Health and Social Care, University of Derby, Cohehre President, United Kingdom)
  • Yao SUYUAN (Heilongjiang University of Traditional Chinese Medicine, Heilongjiang, China)
  • Valérie TÓTHOVÁ (Faculty of Health and Social Sciences, University of South Bohemia, České Budějovice, Czech Republic)
  • Tibor VALYI-NAGY (Department of Pathology, University of Illonois of Chicago, Chicago, IL, United States)
  • Chen ZHEN (Central European TCM Association, European Chamber of Commerce for Traditional Chinese Medicine)

2020  

CrossRef
Documents

9
CrossRef Cites 8
CrossRef H-index 2
Days from submission to acceptance 219
Days from acceptance to publication 176
Acceptance
Rate
47%

 

 

2019  
CrossRef
Documents
13
Acceptance
Rate
83%

 

Developments in Health Sciences
Publication Model Online only Gold Open Access
Submission Fee none
Article Processing Charge none
Subscription Information Gold Open Access

Developments in Health Sciences
Language English
Size A4
Year of
Foundation
2018
Volumes
per Year
1
Issues
per Year
2
Founder Semmelweis Egyetem
Founder's
Address
H-1085 Budapest, Hungary Üllői út 26.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2630-9378 (Print)
ISSN 2630-936X (Online)

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