Wide-field Scans Research Articles

Wide-Field Optical Coherence Tomography Imaging Improves Rate of Change Detection in Progressing Glaucomatous Eyes Compared With Standard-Field Imaging.

To compare rates of retinal nerve fiber layer change over time in healthy, eyes with nonprogressing glaucoma and eyes with progressing glaucoma using single wide-field (SWF) and optic nerve head (ONH) cube scan optical coherence tomography (OCT) images. Forty-five eyes of 25 healthy individuals and 263 eyes of 161 glaucoma patients from the Diagnostic Innovations in Glaucoma Study were included. All eyes underwent 24-2 visual field testing and OCT (Spectralis SD-OCT) ONH and macular imaging. SWF images (up to 43° × 28°) were created by stitching together ONH cube scans centered on the optic disc and macular cube scans centered on the fovea. Visual field progression was defined as guided progression analysis likely progression and/or a significant (P < 0.01) mean deviation slope of less than -1.0dB/year. Mixed effects models were used to compare rates of change. Highly myopic eyes were included. Thirty glaucomatous eyes were classified as progressing. In eyes with glaucoma, mean global rate of change was -1.22 µm/year (P < 0.001) using SWF images and -0.83 µm/year (P = 0.003) using ONH cube scans. Rate of change was significantly greater in eyes with progressing glaucoma compared with eyes with nonprogressing glaucoma (-1.51 µm/year vs. -1.24 µm/year; P = 0.002) using SWF images and was similar using ONH cube scans (P = 0.27). In this cohort that includes eyes with and without high axial myopia, the mean rate of retinal nerve fiber layer thinning measured using SWF images was faster in eyes with progressing glaucoma than in eyes with nonprogressing glaucoma. Wide-field OCT images including the ONH and macula can be effective for monitoring glaucomatous progression in patients with and without high myopia.

Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.

We wanted to develop a deep-learning algorithm to automatically segment optic nerve head (ONH) and macula structures in three-dimensional (3D) wide-field optical coherence tomography (OCT) scans and to assess whether 3D ONH or macula structures (or a combination of both) provide the best diagnostic power for glaucoma. A cross-sectional comparative study was performed using 319 OCT scans of glaucoma eyes and 298 scans of nonglaucoma eyes. Scans were compensated to improve deep-tissue visibility. We developed a deep-learning algorithm to automatically label major tissue structures, trained with 270 manually annotated B-scans. The performance was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D-CNN) was then designed using 500 OCT volumes and corresponding automatically segmented labels. This algorithm was trained and tested on three datasets: cropped scans of macular tissues, those of ONH tissues, and wide-field scans. The classification performance for each dataset was reported using the area under the curve (AUC). Our segmentation algorithm achieved a DC of 0.94 ± 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field scans, followed by ONH scans, and finally macula scans, with AUCs of 0.99 ± 0.01, 0.93 ± 0.06 and 0.91 ± 0.11, respectively. This study showed that wide-field OCT may allow for significantly improved glaucoma diagnosis over typical OCTs of the ONH or macula. This could lead to mainstream clinical adoption of 3D wide-field OCT scan technology.

Early Sign of Retinal Neovascularization Evolution in Diabetic Retinopathy: A Longitudinal OCT Angiography Study

To assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR). Retrospective, longitudinal study. Patients with DR that had at least 2 visits. We obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV. The characteristics and longitudinal observation of early signs of RNV. From 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P= 0.014) and tended to be treatment naive (P= 0.07) compared with eyes without progression. Longitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Toward a Real-world Optical Coherence Tomography Reference Database: Optometric Practices as a Source of Healthy Eyes.

The reports from optical coherence tomography (OCT) instruments depend on a reference database (RDB) of healthy eyes. Although these RDBs tend to be relatively small, they are time consuming and expensive to obtain. A larger RDB should improve our ability to screen for diseases such as glaucoma. To explore the feasibility of developing a large RDB from OCT scans obtained by optometrists as part of their pre-test gathering of information, we tested the hypothesis that these scans are of sufficient quality for an RDB and contain a relatively low base rate of glaucoma and other pathologies (OPs). Optical coherence tomography widefield (12 × 9 mm) scans from 400 eyes of 400 patients were randomly selected from a data set of more than 49,000 scans obtained from four optometry sites. Based on a commercial OCT report and a previously validated reading center method, two OCT graders categorized eyes as unacceptable to use for RDB, healthy (H), optic neuropathy consistent with glaucoma (ON-G), glaucoma suspect, or OPs. Overall, 29 (7.25%) of the eyes were graded unacceptable. Of the remaining 371 eyes, 352 (94.9%) were graded H. Although, for one site, 7.4% of the eligible eyes were graded ON-G, the average for the other three sites was 1.4%. Adjustments of the reading center criteria resulted in exclusion of more than half of these ON-G and OP eyes. The OCT scans obtained from optometry practices as part of their pre-test regimen are of sufficient quality for an RDB and contain a relatively low base rate of glaucoma and OPs. With the suggested exclusion criteria, the scans from optometry practices that are primarily involved in refraction and medical screening services should yield a large, real-world RDB with improved specificity and a base rate of glaucoma and/or OPs comparable with existing RDB.

Multimodal Deep Learning Classifier for Primary Open-angle Glaucoma Diagnosis Using Wide-field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

An OCT-based multimodal deep learning classification model including texture information is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. To evaluate the diagnostic accuracy of a multimodal deep learning (DL) classifier using wide optical coherence tomography (OCT) optic nerve head cube scans in eyes with and without axial high myopia. 371 primary open-angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia (axial length AL≤26mm) and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL>26mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: 1) texture-based en face image, 2) retinal nerve fiber layer (RNFL) thickness map image and 3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves (AUROC) were used to compare model accuracy. Adjusted AUROC for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models (0.83 [0.79, 0.86] for texture based en face image; 0.84 [0.81, 0.87] for RNFL thickness map; and 0.68 [0.61, 0.74] for cSLO image; all P≤0.05). Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy (0.89 [0.86 , 0.92]) compared to texture en face image (0.83 [0.78 , 0.85]), RNFL (0.85 [0.81 , 0.86]) and cSLO image models (0.69 [0.63 , 0.76]) (all P≤0.05). Combining OCT-based RNFL thickness maps with texture based en face images showed better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

The tracking tapered gridded estimator for the power spectrum from drift scan observations

ABSTRACT Intensity mapping with the redshifted 21-cm line is an emerging tool in cosmology. Drift scan observations, where the antennas are fixed to the ground and the telescope’s pointing centre (PC) changes continuously on the sky due to earth’s rotation, provide broad sky coverage and sustained instrumental stability needed for 21-cm intensity mapping. Here, we present the Tracking Tapered Grided Estimator (TTGE) to quantify the power spectrum of the sky signal estimated directly from the visibilities measured in drift scan radio interferometric observations. The TTGE uses the data from the different PC to estimate the power spectrum of the signal from a small angular region located around a fixed tracking centre (TC). The size of this angular region is decided by a suitably chosen tapering window function that serves to reduce the foreground contamination from bright sources located at large angles from the TC. It is possible to cover the angular footprint of the drift scan observations using multiple TC, and combine the estimated power spectra to increase the signal-to-noise ratio. Here, we have validated the TTGE using simulations of $154 \, {\rm MHz}$ Murchison Wide-field Array drift scan observations. We show that the TTGE can recover the input model angular power spectrum Cℓ within $20 {{\ \rm per\ cent}}$ accuracy over the ℓ range 40 &amp;lt; ℓ &amp;lt; 700.

Wide-field swept-source OCTA in the assessment of retinal microvasculature in early-stage diabetic retinopathy

BackgroundTo perform a quantitative analysis of retinal microvasculature in patients with early-stage diabetic retinopathy (DR) using wide-field swept-source optical coherence tomography angiography (SS-OCTA). MethodsOne hundred nineteen eyes of 119 patents (67 eyes with no DR and 52 eyes with mild-moderate nonproliferative diabetic retinopathy (NPDR)) were enrolled in this observational and cross-sectional cohort study, and an age-matched group consisting of 39 eyes of 39 non-diabetic subjects were set as the control. Each participant underwent a full ophthalmic examination, including wide-field SS-OCTA imaging. On OCTA scans (12 mm * 12 mm), the mean perfusion area (PA) and vessel density (VD) were independently measured in all 16 Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. Linear regression analyses were conducted to evaluate the influences of PA.ResultsIn the central ring, there were no significant differences in the average PA and VD among the groups. In the 3 mm radius, the PA and VD of the no DR and mild-moderate NPDR were significantly decreased compared with the control group in superior and inferior quadrants. In the wide-field scans (9 and 12 mm radius), there was no significant difference in average PA and VD between the groups in each sectors (p > 0.05). Regression analysis found that the effect of VD on PA was statistically different (b = 1.311, p < 0.001).ConclusionWide-field OCTA imaging is useful for evaluating peripheral capillary perfusion in eyes with early-stage DR. Decrease in PA and VD was greater in the S3 and I3 sectors, and reductions in PA and VD were uneven in wide-filed sectors (9 and 12 mm radius).

Ultrahigh-speed multimodal adaptive optics system for microscopic structural and functional imaging of the human retina.

We describe the design and performance of a multimodal and multifunctional adaptive optics (AO) system that combines scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) for simultaneous retinal imaging at 13.4 Hz. The high-speed AO-OCT channel uses a 3.4 MHz Fourier-domain mode-locked (FDML) swept source. The system achieves exquisite resolution and sensitivity for pan-macular and transretinal visualization of retinal cells and structures while providing a functional assessment of the cone photoreceptors. The ultra-high speed also enables wide-field scans for clinical usability and angiography for vascular visualization. The FDA FDML-AO system is a powerful platform for studying various retinal and neurological diseases for vision science research, retina physiology investigation, and biomarker development.

Comparison of Diagnostic Ability Between Wide-Field Swept-Source Optical Coherence Tomography Imaging Maps and Heidelberg Retina Tomograph 3 Optic Nerve Head Assessment to Discriminate Glaucomatous and Non-glaucomatous Eyes.

BackgroundIn this study, we aimed to determine the diagnostic performance of optic nerve head (ONH), macular, and circumpapillary retinal nerve fiber layer (cpRNFL) thickness measurements of wide-field maps (12 × 9 mm) using swept-source optical coherence tomography (SS-OCT) compared to measurements of the ONH and RNFL parameters measured by Heidelberg Retina Tomograph (HRT3).MethodologyThis case-control study included 39 eyes of 39 glaucoma patients and 36 eyes of 36 normal subjects (control group). All participants underwent standard automated perimetry (SAP) as well as structural measurements by SS-OCT (DRI-OCT, Triton; Topcon Inc., Tokyo, Japan) and HRT3 (Heidelberg Engineering, Heidelberg, Germany). The abilities of the continuous parameters to discriminate between glaucoma and control groups were assessed using areas under the receiver operating characteristic curves (AUCs). To assess the glaucoma diagnostic abilities of each of the categorical variables, sensitivity, specificity, positive predictive value, and negative predictive value were tested.ResultsThe highest sensitivities were achieved by the DRI-OCT categorical parameters of Superpixel-200 map and cpRNFL (12 sectors) thickness analysis. The best performing HRT3 continuous parameter was rim volume (AUC = 0.829, 95% confidence interval (CI) = 0.735-0.922), and the best continuous parameter for DRI-OCT wide-field was vertical cdr (AUC = 0.883, 95% CI = 0.805-0.951), followed by total cpRNFL thickness (AUC = 0.862, 95% CI = 0.774-0.951). AUCs for disc area, rim area, linear cdr, and RNFL thickness were not significantly different between the two technologies. Using either the most or the least specific criteria, SuperPixel-200 map always showed the highest sensitivity among the categorical parameters of both technologies (82.1% and 89.7%, respectively). The highest sensitivity among HRT3 classification parameters was shown by MRA and GPS classification algorithms.ConclusionsBoth wide-field DRI-OCT maps and HRT3 showed good diagnostic performance in discriminating glaucoma. Although DRI-OCT thickness values and normative diagnostic classification showed the best performance, more studies are required to determine the clinical role of wide-field DRI-OCT scan in glaucoma diagnosis.

The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.

PurposeThe purpose of this study was to improve the diagnostic ability of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by understanding the frequency and pattern of artifacts seen on the p-maps of healthy control (HC) eyes resembling glaucomatous damage.MethodsRNFL p-maps were generated from wide-field OCT cube scans of 2 groups of HC eyes, 200 from a commercial normative group (HC-norm) and 54 from a prospective study group, as well as from 62 patient eyes, which included 32 with early glaucoma (EG). These 32 EG eyes had 24-2 mean deviation (MD) better than −6 dB and perimetric glaucoma as defined by 24-2 and 10-2 criteria. For the HC groups, “glaucoma-like” arcuates were defined as any red region near the temporal half of the disc.ResultsSeven percent of the 200 HC-norm and 11% of the 54 HC RNFL p-maps satisfied the definition of “glaucoma-like,” as did all the patients’ p-maps. The HC p-maps showed two general patterns of abnormal regions, “arcuate” and “temporal quadrant,” and these patterns resembled those seen on some of the RNFL p-maps of the EG eyes. A “vertical midline” rule, which required the abnormal region to cross the vertical midline through the fovea, had a specificity of >99%, and a sensitivity of 75% for EG and 93% for moderate to advanced eyes.ConclusionsGlaucoma-like artifacts on RNFL p-maps are relatively common and can masquerade as arcuate and/or widespread/temporal damage.Translational RelevanceA vertical midline rule had excellent specificity. However, other OCT information is necessary to obtain high sensitivity, especially in eyes with early glaucoma.

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer.

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific.

Combined wide-field optical coherence tomography angiography density map for high myopic glaucoma detection

The present study aimed to evaluate the diagnostic ability of wide-field optical coherence tomography angiography (OCTA) density map for detection of glaucomatous damage in high myopic (HM) eyes and to further compare the diagnostic ability of OCTA with that of conventional imaging approaches including red-free photography and swept-source OCT (SS-OCT) wide-field maps. A total of 77 healthy HM eyes and 72 HM eyes with open angle glaucoma (OAG) participated in this retrospective observational study. Patients underwent a comprehensive ocular examination, including wide-field SS-OCT scan and peripapillary area and macular OCTA scans. An integrated OCTA density map thereafter was merged by vascular landmark-guided superimposition of peripapillary and macular superficial vascular density maps onto the red-free photography (resulting in the OCTA-PanoMap). Glaucoma specialists then determined the presence of glaucomatous damage in HM eyes by reading the OCTA-PanoMap and compared its sensitivity and specificity with those of conventional images. Sensitivity and specificity of OCTA-PanoMap for HM-OAG diagnosis was 94.4% and 96.1%, respectively. Compared with other imaging methods, the sensitivity of OCTA-PanoMap was significantly higher than that of red-free photography (P = 0.022) and comparable to that of wide-field SS-OCT maps. Specificity of OCTA-PanoMap was significantly higher than those of other conventional imaging methods (except for wide-field thickness map). The OCTA-PanoMap showed good diagnostic ability for discrimination of HM-OAG eyes from healthy HM eyes. As a complementary method of an alternative imaging modality, OCTA-PanoMap can be a useful tool for detection of HM-OAG.

Wide-Field Swept-Source OCT Analysis of Interocular Symmetry of Choroidal Thickness in Subjects with Uncomplicated Pachychoroid.

Background: We aimed to study the bilateral choroidal thickness (CT) symmetry and difference in uncomplicated pachychoroid subjects using wide-field swept-source optical coherence tomography (SS-OCT). Methods: All subjects underwent a wide-field 16-mm one-line scan using SS-OCT. Bilateral CT was measured at, and compared among, the following 12 points: three points at 900-µm intervals from the nasal optic disc margin (nasal peripapillary area), one point at the subfovea, six points at 900-µm intervals from the fovea to the nasal and temporal areas (macular area), and two peripheral points 5400 and 8100 µm from the fovea (peripheral area). Results: There were no statistically significant differences in CT between the right and left eyes in any area (all p > 0.05); they all showed significant positive correlations (all p < 0.01). However, the correlation coefficients (ρ) were smaller for the nasal peripapillary and peripheral areas compared to the macular area. Conclusions: The CTs in each region were bilaterally symmetrical in subjects with uncomplicated pachychoroid. However, interocular difference in CT increased from the center to the periphery, indicating that the anatomical variation of the nasal peripapillary and peripheral choroid was greater than that of the macula.

Wide-field optical coherence tomography deviation map for early glaucoma detection

Background/aimsThis study aimed to establish a wide-field optical coherence tomography (OCT) deviation map obtained from swept-source OCT (SS-OCT) scans. Moreover, it also aimed to compare the diagnostic ability of this...

Fast widefield scan provides tunable and uniform illumination optimizing super-resolution microscopy on large fields

Non-uniform illumination limits quantitative analyses of fluorescence imaging techniques. In particular, single molecule localization microscopy (SMLM) relies on high irradiances, but conventional Gaussian-shaped laser illumination restricts the usable field of view to around 40 µm × 40 µm. We present Adaptable Scanning for Tunable Excitation Regions (ASTER), a versatile illumination technique that generates uniform and adaptable illumination. ASTER is also highly compatible with optical sectioning techniques such as total internal reflection fluorescence (TIRF). For SMLM, ASTER delivers homogeneous blinking kinetics at reasonable laser power over fields-of-view up to 200 µm × 200 µm. We demonstrate that ASTER improves clustering analysis and nanoscopic size measurements by imaging nanorulers, microtubules and clathrin-coated pits in COS-7 cells, and β2-spectrin in neurons. ASTER’s sharp and quantitative illumination paves the way for high-throughput quantification of biological structures and processes in classical and super-resolution fluorescence microscopies.

Efficient and high accuracy 3-D OCT angiography motion correction in pathology.

We describe a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features such as retinal layers as well as transverse angiographic vascular features in a joint optimization. Combined with orthogonal scanning and favorization of kinematically more plausible displacements, subpixel alignment and micrometer-scale distortion correction is achieved in all 3 dimensions. As no specific structures are segmented, the method is by design robust to pathologic changes. Furthermore, the method is designed for highly parallel implementation and short runtime, allowing its integration into clinical workflow even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in an extensive quantitative evaluation based on 204 volumetric scans of 17 subjects, including patients with diverse pathologies and healthy controls. Using this method, we achieve state-of-the-art axial motion correction and show significant advances in both transverse co-alignment and distortion correction, especially in the subgroup with pathology.

Current Choroidal Imaging Findings in Central Serous Chorioretinopathy.

Background: Central serous chorioretinopathy (CSCR) is a chorioretinal disease affecting mostly middle age males. It is marked by the serous detachment of the neurosensory layer at the macula. This review of the literature provides a framework of the current characteristic/relevant imaging findings of CSCR. Although the pathogenesis of CSCR is unclear, the choroid plays a major role and its changes are fundamental to the diagnosis and treatment of CSCR. Methods: A systematic literature search focusing on current multimodal imaging for CSCR was performed. Only articles reporting on original clinical data were selected, studies in a language other than English were included only if an English abstract was provided. Additional sources included articles cited in the references list of the first selected articles. We deduced imaging findings based on current and relevant literature on the topic. Results: We found that sub foveal choroidal thickness (SFCT) and choroidal vascularity index (CVI) were greater in eyes with acute CSCR than in eyes with chronic CSCR or normal eyes. There was increased choroidal thickness (CT) in the macula compared to peripapillary region. In healthy eyes, the highest CVI was found in the nasal region followed by the inferior, temporal, and superior quadrant. The area with the least CVI was the macula. In eyes with CSCR, 100% had asymmetric dominant vortex veins compared to 38% in normal eyes. Conclusion: Choroidal imaging has advanced the diagnosis of CSCR. This has led to numerous imaging biomarkers like CVI, CT, and hyper-reflective dots for early detection and possible prognostication of CSCR. More techniques like wide field scans and en face imaging are being employed to characterize the choroid in CSCR.

Quantifying Areas of Vascular Leakage in Sickle Cell Retinopathy Using Standard and Widefield Fluorescein Angiography.

To evaluate neovascular surface area change in sickle cell retinopathy following scatter photocoagulation treatment in wide- and standard-field fluorescein angiography (FA) scans using ImageJ software. Images of 11 patients with wide- or standard-field FA scans pre- and post-treatment for sickle cell retinopathy were evaluated retrospectively by two graders using ImageJ. Graders traced lesions in the late arteriovenous phase and calculated the lesion area and intensity relative to the optic disc. Changes in area and intensity pre- to post-treatment were assessed using dependent t-tests. Pre- to post-treatment, lesion area decreased by 3.34 ± 2.43 to 3.66 ± 3.72 disc areas (P < .001), whereas intensity decreased by 11.36 ± 25.87 to 22.97 ± 69.25 units (P = .104). Neovascular area declined status post-aphotocoagulation by 3.83 ± 3.65 disc areas (P = .003) for widefield images and by 2.81 ± 3.55 discs areas (P = .034) for standard fluorescein images. No statistical difference in area reduction was appreciated between imaging modalities (P = .652) CONCLUSION: Neovascular area decreased significantly following scatter photocoagulation in both standard-field and widefield FA scans without an appreciable difference between imaging modalities. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:153-158.].

Quantitative Microvascular Analysis With Wide-Field Optical Coherence Tomography Angiography in Eyes With Diabetic Retinopathy

Wide-field optical coherence tomographic angiography (OCTA) may provide insights to peripheral capillary dropout in eyes with diabetic retinopathy (DR). To describe the diagnostic performance of wide-field OCTA with and without large vessel removal for assessment of DR in persons with diabetes. This case-control study was performed from April 26, 2018, to April 8, 2019, at a single tertiary eye center in Singapore. Case patients were those with type 2 diabetes for more than 5 years and bilateral DR diagnosed by fundus imaging; control participants included those with no self-reported history of diabetes, a fasting glucose level within the normal range in the past year, and no ocular pathologic findings. A wide-field (12 × 12-mm2) fovea-centered scan was performed using a prototype swept source OCTA system. Retinal microvasculature was examined by separating the angiograms into large vessels, capillaries, and capillary dropout regions. Area under the receiver operating characteristic curve (AUC) for DR severity discrimination using wide-field vascular metrics. Retinal perfusion density (RPD), capillary perfusion density (CPD), large vessel density (LVD), and capillary dropout density (CDD) were calculated. Low-contrast regions were excluded from the calculation. A total of 49 eyes in 27 control participants (17 male [63.0%]; mean [SD] age, 59.96 [7.63] years; age range, 44-79 years) and 76 eyes in 47 patients with diabetes (29 male [61.7%]; mean [SD] age, 64.36 [8.08] years; range, 41-79 years) were included. Among eyes in patients with diabetes, 23 were in those with diabetes but no DR, 25 in those with mild nonproliferative DR, and 28 in those with moderate to severe nonproliferative DR. There was no difference in RPD, CPD, LVD, and CDD between the control group and the group with diabetes and no DR. There was a stepwise decrease in RPD, CPD, and CDD in the diabetes with no DR, mild nonproliferative DR, and moderate to severe nonproliferative DR groups, whereas LVD was not associated with DR staging. The nonproliferative DR group had decreased RPD, CPD, and CDD compared with the control group. The CPD had higher AUCs than RPD for discriminating diabetes with nonproliferative DR (combined mild and moderate to severe nonproliferative DR) vs no DM (AUC, 0.92 [95% CI, 0.87-0.98] vs 0.89 [95% CI, 0.83-0.95], P = .01), diabetes with no DR vs mild nonproliferative DR (AUC, 0.81 [95% CI, 0.68-0.94] vs 0.77 [95% CI, 0.64-0.91], P = .18), and mild nonproliferative DR vs moderate to severe nonproliferative DR (AUC, 0.82 [95% CI, 0.71-0.94] vs 0.78 [95% CI, 0.65-0.91], P = .01) but similar AUCs for no DM vs diabetes with no DR. The total perfusion density and CPD in wide-field OCTA had better discriminative power than the central 6 × 6-mm2 field (CPD, 0.89 [95% CI, 0.83-0.95] vs 0.84 [95% CI, 0.77-0.92], P = .06; total perfusion density, 0.93 [95% CI, 0.87-0.98] vs 0.90 [95% CI, 0.83-0.96], P = .06). The findings suggest that wide-field OCTA provides information on microvascular perfusion and may be useful for detecting predominant peripheral capillary dropout in eyes with nonproliferative DR. A vascular selectivity approach excluding the large vessels may improve the discriminative power for different stages of DR.

Artificial intelligence and its applications in ophthalmology

The term artificial intelligence (AI) was proposed in 1956 by Dartmouth scholar John McCarthy, which refers to hardware or software that exhibits behavior which appears intelligent.1 During recent times, AI gained immense popularity as new algorithms, specialized hardware, huge data and cloud-based services were developed. Machine learning (ML), a subset of AI, originated in 1980 and is defined as a set of methods that automatically detect patterns in data and then incorporate this information to predict future data under uncertain conditions. Another escalating technology of ML called Deep learning (DL), launched in 2000s, is an escalating technology of ML and has revolutionized the world of AI. These technologies are powerful tools utilized by modern society for objects' recognition in images, real-time languages' translation, device manipulation via speech (such as Apple's Siri®, Amazon’s Alexa®, Microsoft’s Cortana®, etc.). The steps for AI model include preprocessing image data, train, validate and test the model, and evaluate the trained model's performance. To increase AI prediction efficiency, raw data need to be preprocessed. Data collected from different sources needs to be integrated and the most relevant features selected and extracted to improve the learning process performance. Data set is randomly partitioned into two independent subsets, one is for modeling and the other is for testing. The test set is used to evaluate the final performance of the trained model. The area under receiver operating characteristic curves (AUC) is most used evaluation metrics for quantitative assessment of a model in AI diagnosis. The AUCs effective models range from 0.5 to 1; higher the value of AUC, better the performance of the model.2 In the medical field, AI gained popularity by visualization of input images of highly potential abnormal sites which can be reviewed and analyzed in future.&#x0D; AI and DL algorithms or systems are also widely used in field of ophthalmology. More intensively studied fields are diabetic retinopathy, age related macular degeneration, and cataract and glaucoma. Various ophthalmic imaging modalities used for AI diagnosis include fundus image, optical coherence tomography (OCT), ocular ultrasound, slit-lamp image and visual field. Diabetic retinopathy (DR), a diabetic complication, is a vasculopathy that affects one-third of diabetic patients leading to irreversible blindness. AI has been in use to predict DR risk and its progression. Gulshan and colleague were the first to report the application of DL for DR identification.3 They used large fundus image data sets in supervised manner for DR detection. Other studies applied DL to identify and stage DR. DL-based computer-aided system was introduced to detect DR through OCT images, achieving a specificity of 0.98.4 A computer-aided diagnostic (CAD) system based on CML algorithms using optical coherence tomography angiography images to automatically diagnose non-proliferative DR (NPDR) also achieved high accuracy and AUC.5 Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among old people in the developed world. ML algorithms are being used to identify AMD lesions and prompt early treatment with accuracy usually over 80%.6 Using ML to predict treatment of retinal neovascularity in AMD and DR by anti-vascular endothelial growth factor (Anti VEGF) injection requirements can manage patients' economic burden and resource management. ML algorithms have been applied to diagnose and grade cataract using fundus images, ultrasounds images, and visible wavelength eye images.7 Glaucoma is the third largest sight-threatening eye disease around the world. Glaucoma patients suffered from high intraocular pressure, damage of the optic nerve head, retina nerve fiber layer defect, and gradual vision loss. Studies using DL methods to diagnose glaucoma are few. So far, fundus images and wide-field OCT scans have all been used to construct DL-based glaucomatous diagnostic models. Mostly, the DL-based methods show excellent results.8&#x0D; In this era of “evidence-based medicine,” clinicians and patients find it difficult to trust a mysterious machine to diagnose yet cannot provide explanations of why the patient has certain disease. In future, advanced AI interpreters will be launched which will contribute significantly to revolutionize current disease diagnostic pattern.