Publications by authors named "Jennifer I Lim"

124 Publications

Prevention of Severe Nonproliferative Diabetic Retinopathy Progression With More at Stake Than Visual Acuity.

Authors:
Jennifer I Lim

JAMA Ophthalmol 2021 Mar 30. Epub 2021 Mar 30.

Department of Ophthalmology, University of Illinois at Chicago, Chicago.

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http://dx.doi.org/10.1001/jamaophthalmol.2021.0625DOI Listing
March 2021

Longitudinal Assessment of Retinal Thinning in Adults With and Without Sickle Cell Retinopathy Using Spectral-Domain Optical Coherence Tomography.

JAMA Ophthalmol 2021 Mar;139(3):330-337

Department of Ophthalmology, University of Illinois at Chicago, Chicago.

Importance: Determination of retinal thinning rates may help to identify patients who are at risk of progression of sickle cell retinopathy.

Objective: To assess the rates of macular thinning in adults with and without sickle cell retinopathy using spectral-domain optical coherence tomography (OCT) and to identify ocular and systemic risk factors associated with retinal thinning.

Design, Setting, And Participants: This longitudinal prospective case-control study enrolled adult participants from a university-based retina subspecialty clinic between February 11, 2009, and July 3, 2019. The study was designed in autumn 2008 and conducted from February 2, 2009, to July 3, 2020. Participants with sickle cell retinopathy (sickle cell group) were matched by age and race with participants without sickle cell retinopathy (control group). Participants received annual spectral-domain OCT and clinical examinations. Those with at least 1 year of follow-up by July 3, 2020, were included in the analysis. Data were analyzed from February 2, 2009, to July 3, 2020.

Main Outcomes And Measures: The primary outcome was comparison of spectral-domain OCT measurements from early-treatment diabetic retinopathy study subfield rates of retinal thinning between eyes with and without sickle cell retinopathy and between different sickle cell hemoglobin subtypes. The secondary outcome was identification of ocular and systemic risk factors associated with rates of retinal thinning.

Results: Among 370 adults (711 eyes) enrolled in the study, 310 participants (606 eyes) had sickle cell retinopathy, and 60 participants (105 eyes) did not. Of those, 175 of 310 participants (56.5%; 344 of 606 eyes [56.8%]; mean [SD] age, 37.8 [12.8] years; 126 women [72.0%]) in the sickle cell group and 31 of 60 participants (51.7%; 46 of 105 eyes [43.8%]; mean [SD] age, 59 [15.4] years; 22 women [71.0%]) in the control group had at least 1 year of clinical and spectral-domain OCT follow-up data from baseline. The mean (SD) follow-up was 53.7 (32.6) months for the sickle cell group and 54.6 (34.9) months for the control group. Rates of macular thinning in the sickle cell group were significantly higher than those in the control group for the inner nasal (difference, -1.18 μm per year; 95% CI, -1.71 to -0.65 μm per year), inner superior (difference, -1.03 μm per year; 95% CI, -1.78 to -0.29 μm per year), inner temporal (difference, -0.61 μm per year; 95% CI, -1.16 to -0.07 μm per year), and outer nasal (difference, -0.41 μm per year; 95% CI, -0.80 to -0.03 μm per year) quadrants. Patients with sickle cell hemoglobin SC and sickle cell hemoglobin β-thalassemia subtypes had higher rates of retinal thinning than those with the sickle cell hemoglobin SS subtype. Risk factors associated with greater rates of retinal thinning included participant age, stage of retinopathy, previous stroke, and presence of hypertension, acute chest syndrome, or diabetes. Hydroxyurea therapy was associated with decreased rates of retinal thinning and may be a protective factor.

Conclusions And Relevance: In this study, rates of retinal thinning were higher among participants with sickle cell retinopathy compared with those without sickle cell retinopathy, and thinning rates increased with participant age and stage of retinopathy. These findings suggest that identifying anatomic worsening of sickle cell maculopathy through spectral-domain OCT may be a useful parameter to evaluate the progression of sickle cell retinopathy.
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http://dx.doi.org/10.1001/jamaophthalmol.2020.6525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863012PMC
March 2021

AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography.

Biomed Opt Express 2020 Sep 25;11(9):5249-5257. Epub 2020 Aug 25.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.
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http://dx.doi.org/10.1364/BOE.399514DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510886PMC
September 2020

Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.

Transl Vis Sci Technol 2020 07 2;9(2):35. Epub 2020 Jul 2.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.

Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy.

Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform.

Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment.

Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients.

Translational Relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.
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http://dx.doi.org/10.1167/tvst.9.2.35DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7424949PMC
July 2020

Reply.

Ophthalmology 2020 08;127(8):e60

Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, California.

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http://dx.doi.org/10.1016/j.ophtha.2020.04.023DOI Listing
August 2020

VASCULAR COMPLEXITY ANALYSIS IN OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF DIABETIC RETINOPATHY.

Retina 2021 Mar;41(3):538-545

Departments of Bioengineering; and.

Purpose: This study aimed to verify the feasibility of using vascular complexity features for objective differentiation of controls and nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) patients.

Methods: This was a cross-sectional study conducted in a tertiary, subspecialty, academic practice. The cohort included 20 control subjects, 60 NPDR patients, and 56 PDR patients. Three vascular complexity features, including the vessel complexity index, fractal dimension, and blood vessel tortuosity, were derived from each optical coherence tomography angiography image. A shifting-window measurement was further implemented to identify local feature distortions due to localized neovascularization and mesh structures in PDR.

Results: With mean value analysis of the whole-image, only the vessel complexity index and blood vessel tortuosity were able to classify NPDR versus PDR patients. Comparative shifting-window measurement revealed increased sensitivity of complexity feature analysis, particularly for NPDR versus PDR classification. A multivariate regression model indicated that the combination of all three vascular complexity features with shifting-window measurement provided the best classification accuracy for controls versus NPDR versus PDR.

Conclusion: Vessel complexity index and blood vessel tortuosity were the most sensitive in differentiating NPDR and PDR patients. A shifting-window measurement increased the sensitivity significantly for objective optical coherence tomography angiography classification of diabetic retinopathy.
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http://dx.doi.org/10.1097/IAE.0000000000002874DOI Listing
March 2021

QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.

Retina 2020 Feb;40(2):322-332

Departments of Bioengineering.

Purpose: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.

Methods: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm.

Results: Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy.

Conclusion: Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.
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http://dx.doi.org/10.1097/IAE.0000000000002373DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494740PMC
February 2020

ADVERSE EVENTS OF THE ARGUS II RETINAL PROSTHESIS: Incidence, Causes, and Best Practices for Managing and Preventing Conjunctival Erosion.

Retina 2020 Feb;40(2):303-311

USC Institute for Biomedical Therapeutics, USC Roski Eye Institute, University of Southern California, Los Angeles, California; and.

Purpose: To analyze and provide an overview of the incidence, management, and prevention of conjunctival erosion in Argus II clinical trial subjects and postapproval patients.

Methods: This retrospective analysis followed the results of 274 patients treated with the Argus II Retinal Prosthesis System between June 2007 and November 2017, including 30 subjects from the US and European clinical trials, and 244 patients in the postapproval phase. Results were gathered for incidence of a serious adverse event, incidence of conjunctival erosion, occurrence sites, rates of erosion, and erosion timing.

Results: Overall, 60% of subjects in the clinical trial subjects versus 83% of patients in the postapproval phase did not experience device- or surgery-related serious adverse events. In the postapproval phase, conjunctival erosion had an incidence rate of 6.2% over 5 years and 11 months. In 55% of conjunctival erosion cases, erosion occurred in the inferotemporal quadrant, 25% in the superotemporal quadrant, and 20% in both. Sixty percent of the erosion events occurred in the first 15 months after implantation, and 85% within the first 2.5 years.

Conclusion: Reducing occurrence of conjunctival erosion in patients with the Argus II Retinal Prosthesis requires identification and minimization of risk factors before and during implantation. Implementing inverted sutures at the implant tabs, use of graft material at these locations as well as Mersilene rather than nylon sutures, and accurate Tenon's and conjunctiva closure are recommended for consideration in all patients.
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http://dx.doi.org/10.1097/IAE.0000000000002394DOI Listing
February 2020

Retinal Vein Occlusions Preferred Practice Pattern®.

Ophthalmology 2020 Feb 25;127(2):P288-P320. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.029DOI Listing
February 2020

Age-Related Macular Degeneration Preferred Practice Pattern®.

Ophthalmology 2020 01 25;127(1):P1-P65. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.024DOI Listing
January 2020

Retinal and Ophthalmic Artery Occlusions Preferred Practice Pattern®.

Ophthalmology 2020 Feb 25;127(2):P259-P287. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.028DOI Listing
February 2020

Posterior Vitreous Detachment, Retinal Breaks, and Lattice Degeneration Preferred Practice Pattern®.

Ophthalmology 2020 01 25;127(1):P146-P181. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.027DOI Listing
January 2020

Idiopathic Macular Hole Preferred Practice Pattern®.

Ophthalmology 2020 Feb 25;127(2):P184-P222. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.026DOI Listing
February 2020

Diabetic Retinopathy Preferred Practice Pattern®.

Ophthalmology 2020 01 25;127(1):P66-P145. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.025DOI Listing
January 2020

Idiopathic Epiretinal Membrane and Vitreomacular Traction Preferred Practice Pattern®.

Ophthalmology 2020 Feb 25;127(2):P145-P183. Epub 2019 Sep 25.

Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

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http://dx.doi.org/10.1016/j.ophtha.2019.09.022DOI Listing
February 2020

Relating retinal blood flow and vessel morphology in sickle cell retinopathy.

Eye (Lond) 2020 05 26;34(5):886-891. Epub 2019 Sep 26.

Ophthalmology, University of Southern California, Los Angeles, CA, United States.

Purpose: The purpose of the current study was to determine associations between retinal blood flow and vessel morphology metrics in sickle cell retinopathy (SCR) and healthy normal control (NC) subjects.

Methods: Optical coherence tomography angiography (OCTA) and Doppler OCT imaging were performed in 12 SCR (15 eyes) and 19 NC (26 eyes) subjects. Vessel tortuosity was measured using a dedicated image analysis algorithm applied to OCTA images. Vessel density and spacing between vessels were determined from OCTA images by a fractal dimension analysis method. Retinal blood flow was quantified using a phase-resolved technique applied to en face Doppler OCT images.

Results: There was a significant association between increased retinal blood flow and increased vessel tortuosity (P = 0.03). Furthermore, increased retinal blood flow was associated with increased vessel density (P = 0.03) and decreased spacing between small vessels (P = 0.01). There was no significant association between retinal blood flow and spacing between large vessels (P = 0.11). Vessel tortuosity and blood flow were increased, whereas spacing between small vessels was decreased in SCR compared to NC group (P ≤ 0.03). There were no significant differences in vessel density or spacing between large vessels between the SCR and NC groups (P ≥ 0.31).

Conclusions: Associations between retinal hemodynamics and vessel morphology were reported, providing better understanding of retinal pathophysiology and insight into potential quantitative biomarkers to evaluate SCR.
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http://dx.doi.org/10.1038/s41433-019-0604-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182580PMC
May 2020

Contrast sensitivity is associated with outer-retina thickness in early-stage diabetic retinopathy.

Acta Ophthalmol 2020 Mar 13;98(2):e224-e231. Epub 2019 Sep 13.

Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA.

Purpose: To determine the relationship between contrast sensitivity (CS) and outer-retina thickness (ORT) in diabetics who have minimal or no diabetic retinopathy (DR).

Methods: Twenty non-diabetic control subjects and 40 type-2 diabetic subjects participated (20 had no clinically apparent DR [NDR] and 20 had mild non-proliferative DR [NPDR]). No subject had a history of treatment for macular oedema. Letter CS, microperimetry (MP) sensitivity and visual acuity (VA) were measured. Letter CS and MP measurements were performed over the central 6° of the visual field. Spectral domain optical coherence tomography (SD-OCT) images were obtained at corresponding locations, outer-retina thickness was quantified, and structure-function relationships were evaluated.

Results: Analysis of variance indicated significant letter CS differences among the groups (p < 0.001). Letter CS was reduced significantly for the mild NPDR group (p < 0.001; 33% reduction), but not the NDR group (p = 0.08). There were no significant differences in MP sensitivity or ORT among the groups (both p > 0.10). Nevertheless, Hoeffding's D tests indicated significant associations between ORT and letter CS (p < 0.001) and between ORT and MP sensitivity for the mild NPDR group (p = 0.01). VA was not significantly associated with ORT for either diabetic group (both p > 0.49).

Conclusions: Outer-retina thickness is associated with letter CS and MP sensitivity, but not VA, in mild NPDR. This finding highlights the usefulness of simple letter CS measures and suggests neural dysfunction can occur in the absence of marked structural abnormalities in early-stage DR.
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http://dx.doi.org/10.1111/aos.14241DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060819PMC
March 2020

Simulating vascular leakage on optical coherence tomography angiography using an overlay technique with corresponding thickness maps.

Br J Ophthalmol 2020 04 5;104(4):514-517. Epub 2019 Jul 5.

UIC Department of Ophthalmology and Visual Sciences, University of Illinois, Chicago, Illinois, USA

Background: To demonstrate a technique for using optical coherence tomography angiography (OCTA) to simulate leakage in eyes with diabetic macular oedema and determine the sensitivity and positive predictive value of detecting leaking microvasculature on OCTA using fluorescein angiography (FA) as the comparative norm.

Methods: 6×6 mm OCT angiograms were overlaid with the corresponding OCT thickness maps. Microvascular abnormalities on the OCT angiogram underlying areas of thickening on the OCT thickness map were assumed to be leaking. Two independent readers blindly read the OCTA overlay images then the FA images cropped to the same approximate region to delineate areas of leaking microvasculature. The results were compared to determine the sensitivity and positive predictive value of OCTA for detection of leaking vessels.

Results: 28 eyes of 19 diabetic patients were included. Each eye demonstrated an average of seven leaking microvascular abnormalities on the OCTA images and 22 leaking abnormalities on the FA images. Sensitivity of leaking microvasculature detection by OCTA was 26.1% and positive predictive value was 68.4%. The correlation coefficient of the two readers' detection of leaking microvasculature was 0.605 for OCTA reads compared with 0.916 for FA.

Conclusion: OCTA as a whole can be used to simulate leakage, but currently, sensitivity of the technique is low. Further understanding of the OCTA technology may yield novel means of detecting retinal pathology.
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http://dx.doi.org/10.1136/bjophthalmol-2019-313976DOI Listing
April 2020

Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies.

J Clin Med 2019 Jun 18;8(6). Epub 2019 Jun 18.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.
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http://dx.doi.org/10.3390/jcm8060872DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617139PMC
June 2019

Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy.

Biomed Opt Express 2019 May 22;10(5):2493-2503. Epub 2019 Apr 22.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

This study is to establish quantitative features of vascular geometry in optical coherence tomography angiography (OCTA) and validate them for the objective classification of diabetic retinopathy (DR). Six geometric features, including total vessel branching angle (VBA: θ), child branching angles (CBAs: α1 and α2), vessel branching coefficient (VBC), and children-to-parent vessel width ratios (VWR1 and VWR2), were automatically derived from each vessel branch in OCTA. Comparative analysis of heathy control, diabetes with no DR (NoDR), and non-proliferative DR (NPDR) was conducted. Our study reveals four quantitative OCTA features to produce robust DR detection and staging classification: (ANOVA, P<0.05), VBA, CBA1, VBC, and VWR1.
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http://dx.doi.org/10.1364/BOE.10.002493DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524582PMC
May 2019

OCT feature analysis guided artery-vein differentiation in OCTA.

Biomed Opt Express 2019 Apr 26;10(4):2055-2066. Epub 2019 Mar 26.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

Differential artery-vein analysis promises better sensitivity for retinal disease detection and classification. However, clinical optical coherence tomography angiography (OCTA) instruments lack the function of artery-vein differentiation. This study aims to verify the feasibility of using OCT intensity feature analysis to guide artery-vein differentiation in OCTA. Four OCT intensity profile features, including i) ratio of vessel width to central reflex, ii) average of maximum profile brightness, iii) average of median profile intensity, and iv) optical density of vessel boundary intensity compared to background intensity, are used to classify artery-vein source nodes in OCT. A blood vessel tracking algorithm is then employed to automatically generate the OCT artery-vein map. Given the fact that OCT and OCTA are intrinsically reconstructed from the same raw spectrogram, the OCT artery-vein map is able to guide artery-vein differentiation in OCTA directly.
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http://dx.doi.org/10.1364/BOE.10.002055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484971PMC
April 2019

Electrophysiological and pupillometric measures of inner retina function in nonproliferative diabetic retinopathy.

Doc Ophthalmol 2019 10 23;139(2):99-111. Epub 2019 Apr 23.

Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, 1855 W. Taylor St., MC/648, Chicago, IL, 60612, USA.

Purpose: To evaluate three measures of inner retina function, the pattern electroretinogram (pERG), the photopic negative response (PhNR), and the post-illumination pupil response (PIPR) in diabetics with and without nonproliferative diabetic retinopathy (NPDR).

Methods: Fifteen non-diabetic control subjects and 45 type 2 diabetic subjects participated (15 have no clinically apparent retinopathy [NDR], 15 have mild NPDR, and 15 have moderate/severe NPDR). The pERG was elicited by a contrast-reversing checkerboard pattern, and the PhNR was measured in response to a full-field, long-wavelength flash presented against a short-wavelength adapting field. The PIPR was elicited by a full-field, 450 cd/m, short-wavelength flash. All responses were recorded and analyzed using conventional techniques. One-way ANOVAs were performed to compare the pERG, PhNR, and PIPR among the control and diabetic groups.

Results: ANOVA indicated statistically significant differences among the control and diabetic subjects for all three measures. Holm-Sidak post hoc comparisons indicated small, nonsignificant reductions in the pERG (8%), PhNR (8%), and PIPR (10%) for the NDR group compared to the controls (all p > 0.25). In contrast, there were significant reductions in the pERG (35), PhNR (34%), and PIPR (30%) for the mild NPDR group compared to the controls (all p < 0.01). Likewise, there were significant reductions in the pERG (40%), PhNR (32%), and PIPR (32%) for the moderate/severe NPDR group compared to the controls (all p < 0.01).

Conclusion: Abnormalities of the pERG, PhNR, and PIPR suggest inner retina neural dysfunction in diabetics who have clinically apparent vascular abnormalities. Taken together, these measures provide a noninvasive, objective approach to study neural dysfunction in these individuals.
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http://dx.doi.org/10.1007/s10633-019-09699-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742572PMC
October 2019

Outcomes of 25-gauge vitrectomy with relaxing retinectomy for retinal detachment secondary to proliferative vitreoretinopathy.

J Vitreoretin Dis 2019 Mar 26;3(2):69-75. Epub 2019 Feb 26.

Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA.

Purpose: The aim of this study is to evaluate visual and anatomic outcomes of 25-gauge vitrectomy with relaxing retinectomies for complex retinal detachment (RD) secondary to proliferative vitreoretinopathy (PVR).

Methods: A single-center, retrospective case series of 44 patients who had undergone a 25-gauge vitrectomy with a relaxing retinectomy for the treatment of combined RD and PVR was performed. Pre-operative characteristics, intraoperative techniques, and outcomes were analyzed. The rates of attachment, complications, and visual acuity were analyzed. Institutional Review Board/Ethics Committee approval was obtained and the described research adhered to the tenets of the Declaration of Helsinki.

Results: At the final follow-up, 27 eyes (61%) had attachment after one surgery, 41 eyes (93%) ultimately had attached retinas, 3 eyes (7%) had hypotony, 3 eyes had become phthisical (7%), and 24 eyes (56%) had improved visual acuity. After stratifying by visual outcomes, 20/400 or better BCVA was not associated with age (=0.66), RD etiology (=0.61), pre-operative hypotony (=0.60), nor size of retinectomy (=0.48). Patients achieving 20/400 vision or better were statistically more likely to be pseudophakic (=0.024) and have silicone oil removal (<0.0001).

Conclusions: The use of 25-gauge vitrectomy and relaxing retinectomy provides a high rate of reattachment and improved visual acuity.
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http://dx.doi.org/10.1177/2474126419831614DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453131PMC
March 2019

Differential Artery-Vein Analysis Improves the Performance of OCTA Staging of Sickle Cell Retinopathy.

Transl Vis Sci Technol 2019 Mar 26;8(2). Epub 2019 Mar 26.

Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.

Purpose: We test if differential artery-vein analysis can increase the performance of optical coherence tomography angiography (OCTA) detection and classification of sickle cell retinopathy (SCR).

Method: This observational case series was conducted in a tertiary-retina practice. Color fundus and OCTA images were collected from 20 control and 48 SCR subjects. Fundus data were collected from fundus imaging devices, and SD-OCT and corresponding OCTA data were acquired using a spectral-domain OCT (SD-OCT) angiography system. For each patient, color fundus image-guided artery-vein classification was conducted in the OCTA image. Traditional mean blood vessel tortuosity (m-BVT) and mean blood vessel caliber (m-BVC) in OCTA images were quantified for control and SCR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (v-BVT) were calculated; and then the artery-vein ratio of BVC (AVR-BVC) and artery-vein ratio of BVT (AVR-BVT) were quantified for comparative analysis.

Results: We evaluated 40 control and 85 SCR images in this study. The color fundus image-guided artery-vein classification had 97.02% accuracy for differentiating arteries and veins in OCTA. Differential artery-vein analysis provided significant improvement ( < 0.05) in detecting and classifying SCR stages compared to traditional mean blood vessel analysis. AVR-BVT and AVR-BVC showed significant ( < 0.001) correlation with SCR severity.

Conclusions: Differential artery-vein analysis can significantly improve the performance of OCTA detection and classification of SCR. AVR-BVT is the most sensitive feature that can classify control and mild SCR.

Translational Relevance: SCR and other retinovascular diseases result in changes to the caliber and tortuosity appearance of arteries and veins separately. Differential artery-vein analysis can improve the performance of SCR detection and stage classification.
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http://dx.doi.org/10.1167/tvst.8.2.3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6438106PMC
March 2019

Bilateral Blurry Vision in a Human Leukocyte Antigen B27-Positive Man.

JAMA Ophthalmol 2019 05;137(5):579-580

Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago.

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http://dx.doi.org/10.1001/jamaophthalmol.2018.6794DOI Listing
May 2019

Longitudinal Study of Peripapillary Thinning in Sickle Cell Hemoglobinopathies.

Am J Ophthalmol 2019 06 14;202:30-36. Epub 2019 Feb 14.

Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA. Electronic address:

Purpose: To determine the rate of retinal nerve fiber layer (RNFL) thinning in patients with sickle cell hemoglobinopathies.

Design: This was a prospective cohort study.

Methods: Sixty-seven patients averaging 35.8 ± 11.5 years of age at enrollment with electrophoretically confirmed sickle cell hemoglobinopathies followed by the University of Illinois at Chicago retina clinic for ≥1 year were included. Exclusion criteria included a history of diabetes, uncontrolled hypertension, glaucoma, ocular opacities, other retinopathies, and previous retinal procedures. The optic nerve head RNFL thicknesses were measured with spectral-domain optical coherence tomography (Heidelberg Engineering, Inc) at enrollment and subsequent follow-ups. Linear mixed models were used to estimate rates of thinning.

Results: A total of 122 eyes were followed for 3.8 ± 2.0 years (range 1-8 years). Mean global peripapillary RNFL thickness was 100.9 ± 13.0 μm at baseline. Global peripapillary RNFL thickness decreased at a rate of 0.98 μm per year (95% confidence interval [CI] 0.77-1.19 μm/year). A history of stroke was associated with a faster rate of global RNFL thinning (1.72 ± 0.20 vs 0.79 ± 0.12 μm/year, P < .001), whereas a history of hypertension was associated with a slower rate of thinning (0.33 ± 0.27 vs 1.14 ± 0.12 μm/year, P = .002).

Conclusions: Peripapillary RNFL thinning in patients with sickle cell hemoglobinopathies occurred faster in patients with a history of stroke and slower in patients with controlled hypertension. Future studies will compare these rates to those of healthy age- and race-matched individuals.
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http://dx.doi.org/10.1016/j.ajo.2019.02.006DOI Listing
June 2019

Association between Visual Acuity and Retinal Layer Metrics in Diabetics with and without Macular Edema.

J Ophthalmol 2018 3;2018:1089043. Epub 2018 Oct 3.

Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.

Purpose: Diabetes is known to cause alterations in retinal microvasculature and tissue that progressively lead to visual impairment. Optical coherence tomography (OCT) is useful for assessment of total retinal thickening due to diabetic macular edema (DME). In the current study, we determined associations between visual acuity (VA) and retinal layer thickness, reflectance, and interface disruption derived from enface OCT images in subjects with and without DME.

Materials And Methods: Best corrected VA was measured and high-density OCT volume scans were acquired in 149 diabetic subjects. A previously established image segmentation method identified retinal layer interfaces and locations of visually indiscernible (disrupted) interfaces. Enface thickness maps and reflectance images of the nerve fiber layer (NFL), combined ganglion cell and inner plexiform layer (GCLIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor outer segment layer (OSL), and retinal pigment epithelium (RPE) were generated in the central macular subfield. The associations among VA and retinal layer metrics were determined by multivariate linear regressions after adjusting for covariates (age, sex, race, HbA1c, diabetes type, and duration) and correcting for multiple comparisons.

Results: In DME subjects, increased GCLIPL and OPL thickness and decreased OSL thickness were associated with reduced VA. Furthermore, increased NFL reflectance and decreased OSL reflectance were associated with reduced VA. Additionally, increased areas of INL and ONL interface disruptions were associated with reduced VA. In subjects without DME, increased INL thickness was associated with reduced VA, whereas in subjects without DME but with previous antivascular endothelium growth factor treatment, thickening of OPL was associated with reduced VA.

Conclusions: Alterations in retinal layer thickness and reflectance metrics derived from enface OCT images were associated with reduced VA with and without presence of DME, suggestive of their potential for monitoring development, progression, and treatment of DME.
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http://dx.doi.org/10.1155/2018/1089043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192089PMC
October 2018

Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography.

Invest Ophthalmol Vis Sci 2018 10;59(12):4953-4962

Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States.

Purpose: This study aimed to develop a method for automated artery-vein classification in optical coherence tomography angiography (OCTA), and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and staging of diabetic retinopathy (DR).

Methods: For each patient, the color fundus image was used to guide the artery-vein differentiation in the OCTA image. Traditional mean blood vessel caliber (m-BVC) and mean blood vessel tortuosity (m-BVT) in OCTA images were quantified for control and DR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (a-BVT) were calculated, and then the artery-vein ratio (AVR) of BVC (AVR-BVC) and AVR of BVT (AVR-BVT) were quantified for comparative analysis. Sensitivity, specificity, and accuracy were used as performance metrics of artery-vein classification. One-way, multilabel ANOVA with Bonferroni's test and Student's t-test were employed for statistical analysis.

Results: Forty eyes of 20 control subjects and 80 eyes of 48 NPDR patients (18 mild, 16 moderate, and 14 severe NPDR) were evaluated in this study. The color fundus image-guided artery-vein differentiation reliably identified individual arteries and veins in OCTA. AVR-BVC and AVR-BVT provided significant (P < 0.001) and moderate (P < 0.05) improvements, respectively, in detecting and classifying NPDR stages, compared with traditional m-BVC analysis.

Conclusions: Color fundus image-guided artery-vein classification provides a feasible method to differentiate arteries and veins in OCTA. Differential artery-vein analysis can improve the sensitivity of OCTA detection and classification of DR. AVR-BVC is the most-sensitive feature, which can classify control and mild NPDR, providing a quantitative biomarker for objective detection of early DR.
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http://dx.doi.org/10.1167/iovs.18-24831DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187950PMC
October 2018