• Users Online: 273
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 17  |  Issue : 2  |  Page : 59-64

Comparative study of the fourier-domain optical coherence tomography structural changes and visual field loss for early detection of glaucoma


1 Department of Ophthalmology, Research Institute of Ophthalmology, Cairo, Egypt
2 Department of Ophthalmology, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt

Date of Submission06-Apr-2016
Date of Acceptance29-May-2016
Date of Web Publication30-Aug-2016

Correspondence Address:
Mona N Mansour
Department of Ophthalmology, Faculty of Medicine (for Girls), Al-Azhar University, Nasr city 11754, Cairo
Egypt
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1110-9173.189473

Rights and Permissions
  Abstract 

Objective
To evaluate the macular ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL) measurements with Fourier-domain optical coherence tomography (FDOCT) for the detection of early glaucoma and to compare the results with visual field (VF) defects.
Design
This was a cross-sectional prospective diagnostic study.
Patients and methods
This study included 10 normal eyes and 48 eyes of patients suspected of having glaucoma on the basis of the appearance of the optic disc. All eyes had normal VFs by standard automated perimetry before the imaging session. A FDOCT system was used to map the macula and peripapillary regions of the retina. Short-wave automated perimetry was performed. Areas under the receiver operating characteristic curves (AUC) were calculated to summarize the diagnostic accuracies of the parameters.
Results
The AUCs for the VF parameters were 0.819 for the mean deviation and 0.911 for the pattern standard deviation. AUCs for the RNFL parameters ranged from 0.625 for the superior average thickness to 0.693 for the inferior average RNFL thickness. AUCs for the macular parameters ranged from 0.467 for the GCC–global loss volume to 0.556 for the inferior GCC thickness. The AUC of the VF parameter with the largest AUC, pattern standard deviation, was significantly higher than that of the RNFL and macular parameters (0.911 vs. 0.693 and 0.556).
Conclusion
GCC measurements with FDOCT were similar to the RNFL parameters, but were inferior to VF deficits on short-wave automated perimetry for early detection of glaucoma.

Keywords: diagnosis, fourier-domain optical coherence tomography, ganglion cell complex, glaucoma, retinal nerve fiber layer, short-wave automated perimetry


How to cite this article:
El-Emary AT, Mohammad JA, Mansour MN. Comparative study of the fourier-domain optical coherence tomography structural changes and visual field loss for early detection of glaucoma. Delta J Ophthalmol 2016;17:59-64

How to cite this URL:
El-Emary AT, Mohammad JA, Mansour MN. Comparative study of the fourier-domain optical coherence tomography structural changes and visual field loss for early detection of glaucoma. Delta J Ophthalmol [serial online] 2016 [cited 2022 May 22];17:59-64. Available from: http://www.djo.eg.net/text.asp?2016/17/2/59/189473


  Introduction Top


Glaucoma is an optic neuropathy that is characterized by progressive loss of the retinal ganglion cells and their axons in the retinal nerve fiber layer (RNFL), thinning of the neuroretinal rim in the optic nerve head (ONH), and visual field (VF) deficit [1].

Although standard automated perimetry (SAP) is considered the clinical standard for VF testing [2], clinical studies have shown that in many cases, VF defects on SAP are detectable only when a considerable number of ganglion cells have been lost [3],[4]. SAP is a relatively nonselective test in that all subtypes of retinal ganglion cells are sensitive to the white target presented on a white background [5]. Short-wavelength automated perimetry (SWAP) is a function-specific test that presents a blue light on a yellow background to emphasize the response characteristics of the blue–yellow pathway [6]. There is evidence that SWAP abnormalities precede SAP VF loss by as much as 4 and 5 years in eyes with ocular hypertension and suspected glaucoma [7].

The use of spectral-domain optical coherence tomography (SDOCT) technology has enabled clinicians to obtain unprecedented high-resolution images of the ONH and RNFL structures in a fraction of the time required by previous technologies. SDOCT can also image the macular area, where the highest concentration of ganglion cells is found, and thus macular imaging has been proposed as a useful tool for the structural assessment of glaucomatous damage [8]. Circumpapillary (cp) RNFL measurements were the parameters that were applied originally to OCT for the diagnosis of glaucoma, but recent studies have shown that ganglion cell complex (GCC) thickness also shows good glaucoma-detecting ability that is comparable with RNFL thickness [9].

The aim of this study was to evaluate and compare the accuracies of Fourier-domain optical coherence tomography (FDOCT) assessment of macular GCC and RNFL, and VF deficits by SWAP for diagnosing preperimetric glaucoma in a cohort of patients suspected of having the disease.


  Patients and methods Top


Participants

Healthy individuals and patients suspected of having glaucoma were selected for this observational cross-sectional study (May 2014–May 2015) from the Outpatient Ophthalmology Clinics at Al-Zahraa University Hospital and Research Institute of Ophthalmology. Fifty eight eyes of 29 patients were included in this study; the patients were 20–50 years of age (mean 38.9±9.49 years). Written informed consent was obtained from each participant after an explanation of the study protocol was provided. The study was approved by the Local Ethics Committee. Each participant underwent a comprehensive ophthalmic examination, including review of medical history, best-corrected visual acuity, slit-lamp biomicroscopy, intraocular pressure measurement using Goldmann applanation tonometry, gonioscopy, dilated fundus examination using a 78 diopter lens, and SAP using a 24-2 Swedish Interactive Threshold Algorithm (Humphrey; Carl Zeiss Meditec Inc., Dublin, California, USA).

To be included, participants had to have a best-corrected visual acuity of 20/40 or better, spherical refraction within ±5.0 diopter, cylinder correction within 3.0 diopter, and open angle on gonioscopy. Participants with a history of ocular trauma or surgery, coexisting retinal disease (such as myopia, age-related macular degeneration, or the presence of macular drusen), uveitis, nonglaucomatous optic disc neuropathy, and diabetic patients were excluded from the study.

The healthy group included individuals with normal findings on ocular examination, no history of elevated intraocular pressure, and full VFs. Full VF was defined as a mean deviation (MD) and pattern standard deviation (PSD) within 95% limits of the normal population and VF glaucoma hemifield test within normal limits. Patients suspected of having glaucoma included individuals with ocular hypertension and/or ONH changes such as neuroretinal rim narrowing, localized notching, cupping, cup asymmetry, or suspicious RNFL defects in the presence of full VF (SAP).

Perimetry

SWAP VFs (Humphrey Field Analyzer; Carl Zeiss Meditec Inc., Dublin, California, USA) were performed using the conventional test procedures (100 cd/m2 yellow background (Schott OG 530 filter; BES Optics Inc., West Warwick, Rhode Island, USA) and a Goldmann size V blue (Omega 440 nm interference filter; Omega Optical, Brattleboro, Vermont, USA) stimulus, full-threshold test strategy, an optimal lens correction was placed before the tested eye, and the fellow eye was occluded with an eye patch. All tests utilized a 24-2 stimulus presentation pattern (a grid of 54 stimulus locations was separated by 6° that bracket the horizontal and vertical meridians).

The glaucomatous VF was defined by two of the following three criteria: the presence of a cluster of three points on pattern deviation probability plot with a P value of less than 5%, one of which had a P value less than 1%, or a PSD with a P value less than 5%, or a glaucoma hemifield test result outside normal limits.

Optical coherence tomography

RTVue (software version 6.1.0.4; Optovue Inc., Fremont, California, USA) was used to obtain SDOCT scans. RTVue uses a superluminescent diode scan with a center wavelength of 840 nm. The instrument can collect 26 000 A-scans per second with an axial resolution of 5 μm. The ONH protocol and GCC protocol were used in this study.

Peripapillary RNFL measurements: the ONH protocol was used to obtain RNFL measurements. This protocol generates an RNFL thickness map measured along a circle 3.45 mm in diameter centered at the ONH. Several subdivisions of the entire measurement circle are performed. First, the overall average, together with the superior hemisphere, inferior hemisphere, temporal quadrant, superior quadrant, nasal quadrant, and inferior quadrant, are provided. Then, each quadrant is divided into two, generating eight sectors of 45°. Finally, each 45° sector is divided into two more sectors, generating 16 sectors of 22.5°. Only good-quality images, as defined by a signal strength index greater than or equal to 28, were included in the analyses.

Macular measurements: the GCC protocol was used to obtain macular measurements. This protocol consists of one horizontal line scan of 7 mm in length (467 A-scans), followed by 15 vertical line scans of 7 mm in length (400 A-scans each) at 0.55 mm intervals. This protocol provides 14 810 A-scans in 0.58 s of a rectangular area. However, the area analyzed by the software involves a 6-mm-diameter circle inside the rectangular area scanned by the instrument. The analyzed circular area is centered 1 mm temporal to the fovea. This slight offset provides a more temporal retina analysis, which corresponds to the nasal VF where glaucomatous damage is most likely to occur at initial stages of the disease (e.g. nasal step). The GCC protocol provides a segmentation of macular B-scans in two layers: the GCC layer and the outer retinal layer. The GCC layer is composed of the ganglion cell layer (GCL), the nerve fiber layer, and the inner plexiform layer (IPL). The GCC layer parameters generated by the GCC protocol are average thickness, superior thickness, inferior thickness, superior minus inferior thickness, global loss volume (GLV), focal loss volume (FLV), and root mean square. GLV measures the average amount of GCC loss over the entire GCC map, whereas FLV measures the average amount of focal GCC loss over the entire GCC map. GLV best detects diffuse ganglion cell loss, similar to MD in VFs. Similarly, FLV best detects local ganglion cell loss using a pattern deviation map to correct for overall absolute changes, similar to PSD in VFs. The root mean square or pattern coefficient of variation provides a summary of how well the fractional map (used to calculate the GLV) and pattern deviation map (used to calculate the FLV) of an individual fit the normal pattern; the worse the fit, the greater the value.

Statistical analysis

Data were statistically described in terms of mean±SD, median and range, or frequencies (number of cases) and percentages when appropriate. Comparison of numerical variables between the study groups was carried out using the Mann–Whitney U-test for independent samples. For comparison of categorical data, the χ2-test was performed. The exact test was used when the expected frequency is less than 5. Accuracy was represented using the terms sensitivity and specificity. Receiver operator characteristic (ROC) analysis was used to determine the optimum cut-off value for the diagnostic markers studied. P values less than 0.05 were considered statistically significant. All statistical calculations were carried out using the computer program statistical package for the social science release 15 for Microsoft Windows, 2006 (SPSS Inc., Chicago, Illinois, USA).


  Results Top


Fifty eight eyes were evaluated for this study: 29 patients suspected of having glaucoma (aged 39.29±9.8 years) and 10 healthy individuals (aged 37.2±8.0 years).

The mean values of the VF parameters, MD and PSD, in the two groups of participants are shown in [Table 1]; significant differences between the two groups were found for both parameters. The areas under the receiver operating characteristic curves (AUCs) were 0.911 for PSD and 0.819 for MD. PSD had a higher sensitivity of 87.5% at a specificity of 80%.
Table 1 Visual field parameters in patients suspected of having glaucoma and healthy eyes with AUC and sensitivities at fixed specificities

Click here to view


The mean values of the RNFL parameters in the two groups of participants are shown in [Table 2]. No significant differences were found between the two groups for any of the parameters. The AUCs for the RNFL parameters ranged from 0.625 for the superior average thickness to 0.693 for the inferior average RNFL thickness. Inferior average thickness had the highest sensitivity of 45.8% at a specificity of 80%.
Table 2 RNFL thickness parameters in patients suspected of having glaucoma and healthy eyes with AUC and sensitivities at fixed specificities

Click here to view


The mean values of macular parameters in the two groups of participants are shown in [Table 3]. No significant differences between the two groups were found for any of the parameters. The AUCs for the macular parameters ranged from 0.467 for the GLV to 0.556 for the inferior GCC thickness. Superior GCC thickness had the highest sensitivity of 22.9% at a specificity of 80%.
Table 3 Macular parameters in patients suspected of having glaucoma and healthy eyes with AUC and sensitivities at fixed specificities

Click here to view


[Figure 1] shows the ROC curves of the best VF, the best RNFL, and the best macular parameter. The AUC of the VF parameter with the largest AUC, PSD, was significantly higher than that of the RNFL and macular parameters. [Figure 2] shows a clinical example of a localized thinning of the GGC and RNFL in a patient suspected of having glaucoma.
Figure 1 Receiver operating characteristic curves of the best visual field (PSD), retinal nerve fiber layer (inferior average RNFL thickness), and macular inner retinal (Sup. GCC) parameters. PSD, pattern standard deviation; RNFL, retinal nerve fiber layer; Sup.GCC, superior ganglion cell complex.

Click here to view
Figure 2 RTVue-100 FDOCT measurements of a right eye showing that superior mGCC and superior cRNFL thicknesses had decreased (P<5%). cRNFL, circumpapillary retinal nerve fiber layer; FDOCT, Fourier-domain optical coherence tomography; FLV, focal loss volume; GCC, ganglion cell complex; GLV, global loss volume; mGCC, macular ganglion cell complex.

Click here to view



  Discussion Top


This study showed that RNFL assessment with the RTVue performed better than the macular assessments with the same instrument for early detection of glaucoma. In previous studies, the total macular thickness was found to be associated significantly with glaucoma; its diagnostic ability was significantly worse than that of cpRNFL thickness [10],[11].

Although there is evidence that the GCL with the IPL measurements may perform as well as RNFL measures for the detection of early glaucoma [12],[13], Nouri-Mahdavi et al. [14] proved that average RNFL thickness performed better than average GCL/IPL measurements for the detection of glaucoma (AUC 0.964 vs. 0.937; P=0.04).

The diagnostic ability of SDOCT macular parameters has shown that the thickness of the GCC offered higher diagnostic power than the total macular thickness in differentiating between perimetric glaucoma and healthy eyes [15],[16] and similar to that of cpRNFL thickness [15],[17],[18]. Kim et al. [19] also found that RNFL and GCC thickness had a similar diagnostic performance in detecting early, moderate, and advanced glaucoma. Akashi et al. [20] found similar AUCs for the average cpRNFL and GCC thicknesses in early glaucomatous eyes and total all-stage glaucomatous eyes when comparing the diagnostic abilities of three different SDOCTs.

The results of the present study are in agreement with those of Lisboa et al. [8], who reported that RNFL measurements performed better than the macular measurements for the detection of preperimetric glaucomatous damage in a cohort of patients suspected of having glaucoma that had been followed for an average of 13 years. AUC for the average RNFL thickness was 0.89 versus 0.79 for GCC average thickness and 0.74 for the vertical cup-to-disc ratio.

In the present study, the inferior RNFL thickness had the highest AUCs for the RNFL parameters, and this is in agreement with Guedes et al. [21], who reported that the inferior RNFL was the parameter for which a statistically significant difference was observed between normal individuals and groups of patients suspected of having glaucoma. Other investigators found that the best performance in differentiating eyes with preperimetric glaucoma from those suspected of having glaucoma was shown by the superior temporal, global, and inferior temporal RNFL thicknesses. The ROC curve areas for these parameters were 0.88, 0.86, and 0.81, respectively [22].

The GLV and FLV are two new parameters that can be calculated using a software program included in the RTVue FDOCT. The GLV measures the average amount of GCC volume loss over the entire recorded GCC field and is analogous to the MD values in the VF tests. The FLV is used to detect focal losses to correct for overall absolute changes, much as the corrected PSD in the VFs [23]. In contrast to this, FLV had no superiority to other GCC parameters in our results and also as stated in previous studies [16],[24]. One possibility is that the damaged nerve fiber area present at the early stage of glaucoma was larger than expected, even though the thickness was only minimally affected [24].

Although the concept of a ‘functional reserve’ followed, which described a functional latency period in the natural history of glaucoma where structural change occurred without functional change [25], the concept of ‘ganglion cell dysfunction’ (rather than death) may explain why, in some patients, perimetric defects precede identifiable structural changes. In early stages of ganglion cell insult, cells may become dysfunctional, leading to a reduction in VF sensitivity, so that ‘measured structure’ may not be representative of functioning ganglion cell or axonal number [26]. Therefore, the concept of ganglion cell dysfunction may explain our results as it was found that the AUC of the VF parameter by SWAP with the largest AUC, PSD, was higher than that of the RNFL and macular parameters. SWAP has been reported to show closer agreement with early nerve fiber layer damage than SAP [7]. The SWAP shows a specificity of 93–100% in patients suspected of having glaucoma and a combination of RNFL assessment and SWAP analysis yields 100% sensitivity [27].


  Conclusion Top


GCC measurements with RTVue FDOCT were similar to the RNFL parameters, but inferior to VF deficits on SWAP for early detection of glaucoma. Adding SWAP to each of the best structural parameters led to a significant increase in sensitivity compared with each structural parameter alone. However, further studies with larger sample sizes, applying cut-off points with follow-up of patients, are recommended.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Sung KR, Wollstein G, Kim NR, Na JH, Nevins JE, Kim CY, Schuman JS. Macular assessment using optical coherence tomography for glaucoma diagnosis. Br J Ophthalmol 2012;96:1452–1455.  Back to cited text no. 1
    
2.
San Laureano J. When is glaucoma really glaucoma? Clin Exp Optom 2007;90:376–385.  Back to cited text no. 2
    
3.
Anderson RS. The psychophysics of glaucoma: improving the structure/function relationship. Prog Retin Eye Res 2006;25:79–97.  Back to cited text no. 3
    
4.
White AJ, Sun H, Swanson WH, Lee BB. An examination of physiological mechanisms underlying the frequency-doubling illusion. Invest Ophthalmol Vis Sci 2002;43:3590–3599.  Back to cited text no. 4
    
5.
Sample PA, Medeiros FA, Racette L, Pascual JP, Boden C, Zangwill LM et al. Identifying glaucomatous vision loss with visual-function-specific perimetry in the diagnostic innovations in glaucoma study. Invest Ophthalmol Vis Sci 2006;47:3381–3389.  Back to cited text no. 5
    
6.
Racette L, Sample PA. Short-wavelength automated perimetry. Ophthalmol Clin North Am 2003; 16: 227–236.  Back to cited text no. 6
    
7.
Jampel HD, Singh K, Lin SC, Chen TC, Francis BA, Hodapp E et al. Assessment of visual function in glaucoma: a report by the American Academy of Ophthalmology. Ophthalmology 2011;118:986–1002.  Back to cited text no. 7
    
8.
Lisboa R, Paranhos A Jr, Weinreb RN, Zangwill LM, Leite MT, Medeiros FA. Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. Invest Ophthalmol Vis Sci 2013;54:3417–3425.  Back to cited text no. 8
    
9.
Garas A, Vargha P, Holló G. Diagnostic accuracy of nerve fibre layer, macular thickness and optic disc measurements made with the RTVue-100 optical coherence tomograph to detect glaucoma. Eye (Lond) 2011;25:57–65.  Back to cited text no. 9
    
10.
Leung CK, Chan WM, Yung WH, Ng AC, Woo J, Tsang MK, Tse RK Comparison of macular and peripapillary measurements for the detection of glaucoma: an optical coherence tomography study. Ophthalmology 2005;112:391–400.  Back to cited text no. 10
    
11.
Medeiros FA, Zangwill LM, Bowd C, Vessani RM, Susanna R Jr, Weinreb RN. Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol 2005;139:44–55.  Back to cited text no. 11
    
12.
Mwanza JC, Durbin MK, Budenz DL, Sayyad FE, Chang RT, Neelakantan A et al. Glaucoma diagnostic accuracy of ganglion cell-inner plexiform layer thickness: comparison with nerve fiber layer and optic nerve head. Ophthalmology 2012;119:1151–1158.  Back to cited text no. 12
    
13.
Kotowski J, Folio LS, Wollstein G, Ishikawa H, Ling Y, Bilonick RA et al. Glaucoma discrimination of segmented cirrus spectral domain optical coherence tomography (SD-OCT) macular scans. Br J Ophthalmol 2012;96:1420–1425.  Back to cited text no. 13
    
14.
Nouri-Mahdavi K, Nowroozizadeh S, Nassiri N, Cirineo N, Knipping S, Giaconi J, Caprioli J. Macular ganglion cell/inner plexiform layer measurements by spectral domain optical coherence tomography for detection of early glaucoma and comparison to retinal nerve fiber layer measurements. Am J Ophthalmol 2013;156:1297–1307.  Back to cited text no. 14
    
15.
Mori S, Hangai M, Sakamoto A, Yoshimura N. Spectral-domain optical coherence tomography measurement of macular volume for diagnosing glaucoma. J Glaucoma 2010;19:528–534.  Back to cited text no. 15
    
16.
Tan O, Chopra V, Lu AT, Schuman JS, Ishikawa H, Wollstein G et al. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology 2009;116:2305–2314.  Back to cited text no. 16
    
17.
Nakatani Y, Higashide T, Ohkubo S, Takeda H, Sugiyama K. Evaluation of macular thickness and peripapillary retinal nerve fiber layer thickness for detection of early glaucoma using spectral domain optical coherence tomography. J Glaucoma 2011;20:252–259.  Back to cited text no. 17
    
18.
Seong M, Sung KR, Choi EH, Kang SY, Cho JW, Um TW et al. Macular and peripapillary retinal nerve fiber layer measurements by spectral domain optical coherence tomography in normal-tension glaucoma. Invest Ophthalmol Vis Sci 2010;51:1446–1452.  Back to cited text no. 18
    
19.
Kim NR, Lee ES, Seong GJ, Kim JH, An HG, Kim CY Structure-function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma. Invest Ophthalmol Vis Sci 2010;51:4646–4651.  Back to cited text no. 19
    
20.
Akashi A, Kanamori A, Nakamura M, Fujihara M, Yamada Y, Negi A. Comparative assessment for the ability of Cirrus, RTVue, and 3D-OCT to diagnose glaucoma. Invest Ophthalmol Vis Sci 2013;54:4478–4484.  Back to cited text no. 20
    
21.
Guedes V, Schuman JS, Hertzmark E, Wollstein G, Correnti A, Mancini R et al. Optical coherence tomography measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes. Ophthalmology 2003;110:177–189.  Back to cited text no. 21
    
22.
Lisboa R, Leite MT, Zangwill LM, Tafreshi A, Weinreb RN, Medeiros FA. Diagnosing preperimetric glaucoma with spectral domain optical coherence tomography. Ophthalmology 2012;119:2261–2269.  Back to cited text no. 22
    
23.
Rao HL, Kumbar T, Addepalli UK, Bharti N, Senthil S, Choudhari NS, Garudadri CS. Effect of spectrum bias on the diagnostic accuracy of spectral-domain optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci 2012;53:1058–1065.  Back to cited text no. 23
    
24.
Arintawati P, Sone T, Akita T, Tanaka J, Kiuchi Y. The applicability of ganglion cell complex parameters determined from SD-OCT images to detect glaucomatous eyes. J Glaucoma 2013;22:713–718.  Back to cited text no. 24
    
25.
Johnson CA. Selective versus nonselective losses in glaucoma. J Glaucoma 1994;3(Suppl 1):S32–S44.  Back to cited text no. 25
    
26.
Harwerth RS, Vilupuru AS, Rangaswamy NV, Smith EL 3rd. The relationship between nerve fiber layer and perimetry measurements. Invest Ophthalmol Vis Sci 2007;48:763–773.  Back to cited text no. 26
    
27.
Polo V, Larrosa JM, Pinilla I, Perez S, Gonzalvo F, Honrubia FM. Predictive value of short-wavelength automated perimetry: a 3-year follow-up study Ophthalmology 2002;109:761–765.  Back to cited text no. 27
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]


This article has been cited by
1 Optical coherence tomography indices for diagnosis of chronic glaucoma in patients with diabetes mellitus: a pilot study
Fatma K. Hassan,Karim Adly Raafat,Khaled E. Elrakhawy,Riham S. H. M. Allam
International Ophthalmology. 2020;
[Pubmed] | [DOI]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Patients and methods
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed1810    
    Printed79    
    Emailed0    
    PDF Downloaded159    
    Comments [Add]    
    Cited by others 1    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]