Validation of a deep learning model for automatic detection and quantification of five OCT critical retinal features associated with neovascular age-related macular degeneration.

PURPOSE: To develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular degeneration (nAMD). METHODS: 300 optical coherence tomography volumes from subject eyes with nAMD were collected. Images were manually segmented for the presence of five crucial nAMD features: intraretinal fluid, subretinal fluid, subretinal hyperreflective material, drusen/drusenoid pigment epithelium detachment (PED) and neovascular PED. A deep learning architecture based on a U-Net was trained to perform automatic segmentation of these retinal biomarkers and evaluated on the sequestered data. The main outcome measures were receiver operating characteristic curves for detection, summarised using the area under the curves (AUCs) both on a per slice and per volume basis, correlation score, enface topography overlap (reported as two-dimensional (2D) correlation score) and Dice coefficients. RESULTS: The model obtained a mean (±SD) AUC of 0.93 (±0.04) per slice and 0.88 (±0.07) per volume for fluid detection. The correlation score (R2) between automatic and manual segmentation obtained by the model resulted in a mean (±SD) of 0.89 (±0.05). The mean (±SD) 2D correlation score was 0.69 (±0.04). The mean (±SD) Dice score resulted in 0.61 (±0.10). CONCLUSIONS: We present a fully automated segmentation model for five features related to nAMD that performs at the level of experienced graders. The application of this model will open opportunities for the study of morphological changes and treatment efficacy in real-world settings. Furthermore, it can facilitate structured reporting in the clinic and reduce subjectivity in clinicians' assessments.

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD). METHODS: In this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2. Area under the curve (AUC) values gauged detection performance, and quantification between readers and models was evaluated using Dice and correlation (R2) coefficients. RESULTS: The deep learning-based algorithms had high accuracies for all fluid types between all models and readers: per B-scan IRF AUCs were 0.953, 0.932, 0.990, 0.942 for comparisons A1-R1, A1-R2, A2-R1 and A2-R2, respectively; SRF AUCs were 0.984, 0.974, 0.987, 0.979; and nPED AUCs were 0.963, 0.969, 0.961 and 0.966. Similarly, the R2 coefficients for IRF were 0.973, 0.974, 0.889 and 0.973; SRF were 0.928, 0.964, 0.965 and 0.998; and nPED were 0.908, 0.952, 0.839 and 0.905. The Dice coefficients for IRF averaged 0.702, 0.667, 0.649 and 0.631; for SRF were 0.699, 0.651, 0.692 and 0.701; and for nPED were 0.636, 0.703, 0.719 and 0.775. In an inter-observer comparison between manual readers R1 and R2, the R2 coefficient was 0.968 for IRF, 0.960 for SRF, and 0.906 for nPED, with Dice coefficients of 0.692, 0.660 and 0.784 for the same features. CONCLUSIONS: Our deep learning-based method applied on nAMD can segment critical OCT features with performance akin to manual grading.

Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion.

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.

QUANTITATIVE ASSESSMENT OF AUTOMATED OPTICAL COHERENCE TOMOGRAPHY IMAGE ANALYSIS USING A HOME-BASED DEVICE FOR SELF-MONITORING NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To evaluate a prototype home optical coherence tomography device and automated analysis software for detection and quantification of retinal fluid relative to manual human grading in a cohort of patients with neovascular age-related macular degeneration. METHODS: Patients undergoing anti-vascular endothelial growth factor therapy were enrolled in this prospective observational study. In 136 optical coherence tomography scans from 70 patients using the prototype home optical coherence tomography device, fluid segmentation was performed using automated analysis software and compared with manual gradings across all retinal fluid types using receiver-operating characteristic curves. The Dice similarity coefficient was used to assess the accuracy of segmentations, and correlation of fluid areas quantified end point agreement. RESULTS: Fluid detection per B-scan had area under the receiver-operating characteristic curves of 0.95, 0.97, and 0.98 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively. On a per volume basis, the values for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid were 0.997, 0.998, and 0.998, respectively. The average Dice similarity coefficient values across all B-scans were 0.64, 0.73, and 0.74, and the coefficients of determination were 0.81, 0.93, and 0.97 for intraretinal fluid, subretinal fluid, and subretinal pigment epithelium fluid, respectively. CONCLUSION: Home optical coherence tomography device images assessed using the automated analysis software showed excellent agreement to manual human grading.

Corneal Nerve Changes Observed by In Vivo Confocal Microscopy in Patients Receiving Oxaliplatin for Colorectal Cancer: The COCO Study.

An objective method of early identification of people at risk of chemotherapy-induced peripheral neuropathy is needed to minimize long-term toxicity and maximize dose intensity. The aims of the study were to observe corneal nerve microstructure and corneal sensitivity changes and peripheral neuropathy in patients receiving oxaliplatin, and to determine its association with corneal parameters at different stages of treatment and assess utility as non-invasive markers to detect and monitor peripheral neuropathy. Twenty-three patients scheduled to receive oxaliplatin chemotherapy with intravenous 5-FU for gastro-intestinal cancer were recruited and followed up with for 12 months. Ocular examinations including corneal and retinal evaluations, alongside peripheral neuropathy assessment, were performed. The corneal nerve density did not show significant change after chemotherapy when measured with a widely used semi-automated program or an automated analysis technique. Macula and optic nerve function did not change during or after oxaliplatin chemotherapy. However, the corneal nerve density modestly correlated with clinical peripheral neuropathy after 20 weeks of chemotherapy (r = 0.61, p = 0.01) when peripheral neuropathy is typical most profound, and corneal nerve sensitivity correlated with neuropathy at 12 (r = 0.55, p = 0.01) and 20 weeks (r = 0.64, p = 0.006). In conclusion, corneal changes detected on confocal microscopy show moderate association with peripheral neuropathy, indicating their potential to identify the development of oxaliplatin-induced peripheral neuropathy. However, further studies are required to confirm these findings.

Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.

BACKGROUND AND OBJECTIVE: To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images. PATIENTS AND METHODS: This was a proof-of-concept consecutive case series. Patients with an idiopathic full-thickness MH undergoing pars plana vitrectomy surgery with 1 year of follow-up were considered for inclusion. MHs were manually graded by a vitreoretinal surgeon from preoperative OCT images to delineate MH volume. This information was used to train a fully three-dimensional convolutional neural network for automatic segmentation. The main outcome was the correlation of manual MH volume to automated volume segmentation. RESULTS: The correlation between manual and automated MH volume was R2 = 0.94 (n = 24). Automated MH volume demonstrated a higher correlation to change in visual acuity from preoperative to the postoperative 1-year time point compared with the minimum linear diameter (volume: R2 = 0.53; minimum linear diameter: R2 = 0.39). CONCLUSION: MH automated volume segmentation on OCT imaging demonstrated high correlation to manual MH volume measurements. [Ophthalmic Surg Lasers Imaging Retina. 2022;53(4):208-214.].

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.

PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. METHODS: In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. RESULTS: Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). CONCLUSIONS: In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.

Corneal confocal microscopy demonstrates axonal loss in different courses of multiple sclerosis.

Axonal loss is the main determinant of disease progression in multiple sclerosis (MS). This study aimed to assess the utility of corneal confocal microscopy (CCM) in detecting corneal axonal loss in different courses of MS. The results were confirmed by two independent segmentation methods. 72 subjects (144 eyes) [(clinically isolated syndrome (n = 9); relapsing-remitting MS (n = 20); secondary-progressive MS (n = 22); and age-matched, healthy controls (n = 21)] underwent CCM and assessment of their disability status. Two independent algorithms (ACCMetrics; and Voxeleron deepNerve) were used to quantify corneal nerve fiber density (CNFD) (ACCMetrics only), corneal nerve fiber length (CNFL) and corneal nerve fractal dimension (CNFrD). Data are expressed as mean ± standard deviation with 95% confidence interval (CI). Compared to controls, patients with MS had significantly lower CNFD (34.76 ± 5.57 vs. 19.85 ± 6.75 fibers/mm2, 95% CI - 18.24 to - 11.59, P < .0001), CNFL [for ACCMetrics: 19.75 ± 2.39 vs. 12.40 ± 3.30 mm/mm2, 95% CI - 8.94 to - 5.77, P < .0001; for deepNerve: 21.98 ± 2.76 vs. 14.40 ± 4.17 mm/mm2, 95% CI - 9.55 to - 5.6, P < .0001] and CNFrD [for ACCMetrics: 1.52 ± 0.02 vs. 1.45 ± 0.04, 95% CI - 0.09 to - 0.05, P < .0001; for deepNerve: 1.29 ± 0.03 vs. 1.19 ± 0.07, 95% - 0.13 to - 0.07, P < .0001]. Corneal nerve parameters were comparably reduced in different courses of MS. There was excellent reproducibility between the algorithms. Significant corneal axonal loss is detected in different courses of MS including patients with clinically isolated syndrome.

Effects of subthreshold nanosecond laser therapy in age-related macular degeneration using artificial intelligence (STAR-AI Study).

PURPOSE: To investigate changes in retinal thickness, drusen volume, and visual acuity following subthreshold nanosecond laser (SNL) treatment in patients with age-related macular degeneration (ARMD). DESIGN: Retrospective chart review. METHODS: Patients with intermediate ARMD treated with a single session of SNL (2RT®, Ellex R&D Pty Ltd, Adelaide, Australia) were included. Swept-source optical coherence tomography (OCT) imaging (Triton; Topcon Medical Systems, Tokyo, Japan) was performed within 6 months before and after SNL treatment. Retinal layers were segmented using the artificial intelligence-enabled Orion® software (Voxeleron LLC, San Francisco, USA). The macular region was analyzed according to the Early Treatment Diabetic Retinopathy Study map. Mean difference and standard deviation in baseline and post-treatment retinal layer thicknesses are reported. RESULTS: 37 eyes from 25 patients were included in this study (mean age 74.7±9.2 years). An average of 51±6 spots were applied around the macula of each study eye, with a mean spot power of 0.33±0.04mJ. Increases in total retinal thickness were observed within the outer temporal and inferior sectors (P<0.05). Within the annulus, there was an increase in thickness of the sub-retinal pigment epithelial (RPE) space [0.88±2.41µm, P = 0.03], defined between the RPE and Bruch's membrane. An increase in thickness of 1.13±2.55µm (P = 0.01) was also noted in the inferior sector of the photoreceptor complex, defined from the inner and outer segment junction to the RPE. Decreases in thickness were observed within the superior sector of the inner nuclear layer (INL) [-1.08±2.55µm, P = 0.01], and within the annulus of the outer nuclear layer (ONL) [-1.44±3.55µm, P = 0.02]. CONCLUSIONS: At 6 months post-SNL treatment, there were sectoral increases in OPL, photoreceptor complex, and sub-RPE space thicknesses and sectoral decreases in INL and ONL thicknesses. This pilot study demonstrates the utility of OCT combined with artificial intelligence-enabled software to track retinal changes that occur following SNL treatment in intermediate ARMD.

Combining In Vivo Corneal Confocal Microscopy With Deep Learning-Based Analysis Reveals Sensory Nerve Fiber Loss in Acute Simian Immunodeficiency Virus Infection.

PURPOSE: To characterize corneal subbasal nerve plexus features of normal and simian immunodeficiency virus (SIV)-infected macaques by combining in vivo corneal confocal microscopy (IVCM) with automated assessments using deep learning-based methods customized for macaques. METHODS: IVCM images were collected from both male and female age-matched rhesus and pigtailed macaques housed at the Johns Hopkins University breeding colony using the Heidelberg HRTIII with Rostock Corneal Module. We also obtained repeat IVCM images of 12 SIV-infected animals including preinfection and 10-day post-SIV infection time points. All IVCM images were analyzed using a deep convolutional neural network architecture developed specifically for macaque studies. RESULTS: Deep learning-based segmentation of subbasal nerves in IVCM images from macaques demonstrated that corneal nerve fiber length and fractal dimension measurements did not differ between species, but pigtailed macaques had significantly higher baseline corneal nerve fiber tortuosity than rhesus macaques (P = 0.005). Neither sex nor age of macaques was associated with differences in any of the assessed corneal subbasal nerve parameters. In the SIV/macaque model of human immunodeficiency virus, acute SIV infection induced significant decreases in both corneal nerve fiber length and fractal dimension (P = 0.01 and P = 0.008, respectively). CONCLUSIONS: The combination of IVCM and robust objective deep learning analysis is a powerful tool to track sensory nerve damage, enabling early detection of neuropathy. Adapting deep learning analyses to clinical corneal nerve assessments will improve monitoring of small sensory nerve fiber damage in numerous clinical settings including human immunodeficiency virus.

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance. Methods: There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively. Results: The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively. Conclusions: A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia. Translational Relevance: Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.

Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images.

BACKGROUND: To develop and validate a deep learning-based approach to the fully-automated analysis of macaque corneal sub-basal nerves using in vivo confocal microscopy (IVCM). METHODS: IVCM was used to collect 108 images from 35 macaques. 58 of the images from 22 macaques were used to evaluate different deep convolutional neural network (CNN) architectures for the automatic analysis of sub-basal nerves relative to manual tracings. The remaining images were used to independently assess correlations and inter-observer performance relative to three readers. RESULTS: Correlation scores using the coefficient of determination between readers and the best CNN averaged 0.80. For inter-observer comparison, inter-correlation coefficients (ICCs) between the three expert readers and the automated approach were 0.75, 0.85 and 0.92. The ICC between all four observers was 0.84, the same as the average between the CNN and individual readers. CONCLUSIONS: Deep learning-based segmentation of sub-basal nerves in IVCM images shows high to very high correlation to manual segmentations in macaque data and is indistinguishable across readers. As quantitative measurements of corneal sub-basal nerves are important biomarkers for disease screening and management, the reported work offers utility to a variety of research and clinical studies using IVCM.

Deep Learning for Prediction of AMD Progression: A Pilot Study.

Purpose: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning. Methods: Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n = 40) at year 2 and those that had (n = 31). Two deep convolutional neural networks (CNN) were evaluated using 5-fold cross validation on the OCT data at baseline to attempt to predict which eyes would convert to advanced AMD at year 2: (1) VGG16, a popular CNN for image recognition was fine-tuned, and (2) a novel, simplified CNN architecture was trained from scratch. Preprocessing was added in the form of a segmentation-based normalization to reduce variance in the data and improve performance. Results: Our new architecture, AMDnet, with preprocessing, achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 at the B-scan level and 0.91 for volumes. Results for VGG16, an established CNN architecture, with preprocessing were 0.82 for B-scans/0.87 for volumes versus 0.66 for B-scans/0.69 for volumes without preprocessing. Conclusions: A CNN with layer segmentation-based preprocessing shows strong predictive power for the progression of early/intermediate AMD to advanced AMD. Use of the preprocessing was shown to improve performance regardless of the network architecture.

Changes in volume of various retinal layers over time in early and intermediate age-related macular degeneration.

PURPOSE: To evaluate longitudinally volume changes in inner and outer retinal layers in early and intermediate age-related macular degeneration (AMD) compared to healthy control eyes using optical coherence tomography (OCT). METHODS: 71 eyes with AMD and 31 control eyes were imaged at two time points: baseline and after 2 years. Automated OCT layer segmentation was performed using OrionTM. This software is able to measure volumes of retinal layers with distinct boundaries including Retinal Nerve Fibre Layer (RNFL), Ganglion Cell-Inner Plexiform Layer (GCIPL), Inner Nuclear Layer (INL), Outer Plexiform Layer (OPL), Outer Nuclear Layer (ONL), Photoreceptors (PR) and Retinal Pigment Epithelium-Bruch's Membrane complex (RPE-BM). The mean retinal layer volumes and volume changes at 2 years were compared between groups. RESULTS: Mean GCIPL and INL volumes were lower, while PR and RPE-BM volumes were higher in AMD eyes than controls at baseline (all P < 0.05) and year 2 (all P < 0.05). In AMD eyes, RNFL and ONL volumes decreased by 0.0232 (P = 0.033) and 0.0851 (P = 0.001), respectively. In contrast, OPL and RPE-BM volumes increased in AMD eyes by 0.0391 (P = 0.000) and 0.0209 (P = 0.000) respectively. Moreover, there were significant differences in longitudinal volume change of OPL (P = 0.02), ONL (P = 0.008) and RPE-BM (P = 0.02) between AMD eyes and controls. CONCLUSIONS: There were abnormal retinal layer volumes and volume changes in eyes with early and intermediate AMD.

The measurement repeatability using different partition methods of intraretinal tomographic thickness maps in healthy human subjects.

PURPOSE: To determine the repeatability and profiles with different partition methods in intraretinal thickness layers in healthy human subjects, using optical coherence tomography (OCT). METHODS: A custom-built ultrahigh-resolution OCT was used to acquire three-dimensional volume of the macula in 20 healthy subjects. The dataset was acquired twice using the macular cube 512×128 protocol in an area of 6×6 mm2 centered on the fovea. Commercially available segmentation software (Orion™) was used to segment the dataset into thickness maps of six intraretinal layers. The coefficient of repeatability and intraclass coefficient of correlation (ICC) were analyzed using hemispheric zoning and sectors defined by the Early Treatment Diabetic Retinopathy Study (ETDRS). RESULTS: All datasets were successfully segmented to create six thickness maps of individual intraretinal layers. Coefficients of repeatabilities of these layers in hemispheric zones ranged from 0.9 to 6.6 µm, with an average of 3.6 µm (standard deviation [SD] 1.4), which was not significantly different compared to ETDRS sectors (P>0.05). ICCs of these layers in hemispheric zones ranged from 0.68 to 0.99, with an average of 0.91 (SD 0.07). There were no significant differences in ICCs between two zoning methods (P>0.05). Significant variations of tomographic intraretinal thicknesses were found between the inner and outer annuli and among the quadrantal sectors within the inner and outer annuli (P<0.05). Significant variations of the quadrantal sectors including both inner and outer annuli were evident in intraretinal layers (P<0.05) except for the outer plexiform layer. CONCLUSION: The measurement repeatabilities of tomographic thicknesses of intraretinal layers are comparable using both hemispheric and ETDRS partitions in volumetric data combined with the commercially available segmentation software. In keeping with known, normal anatomical variation, significant differences in tomographic thickness in various intraretinal layers were apparent in both hemispheric and ETDRS sectors.

Changes in macular layers in the early course of non-arteritic ischaemic optic neuropathy.

PURPOSE: To characterise the changes of the retinal layers in patients with acute anterior ischaemic optic neuropathy (AION), aiming to identify imaging markers for predicting the residual visual function. METHODS: This was a retrospective review of consecutive patients with unilateral AION from January 2010 to December 2013. We analysed affected eyes at baseline and 1 month later, compared to fellow healthy eyes. Utilising novel image analysis software, we conducted algorithmic segmentation in layers and division in early treatment of diabetic retinopathy study (ETDRS) quadrants of optical coherence tomography images of the macula. Pearson product moment regression analysis of retinal layer thickness and best corrected visual acuity (BCVA) in logMAR units and mean deviation of the SITA 24-2 visual field (VF) were carried out at the 1-month time point. RESULTS: Twenty eyes from 20 patients were included and compared to 20 healthy fellow eyes. At baseline, we found a significantly increased mean thickness of the retinal nerve fibre layer (RNFL) of 42.2 µm (±6.7SD) in AION eyes compared to 37.9 µm (±4.2 SD) in healthy eyes (p = 0.002). The outer nuclear layer (ONL) was also significantly thickened at 96.6 µm (±7.2 SD) compared to 90.8 µm (±5.7 SD) in the fellow eye (p < 0.001). After 1 month, the RNFL and the ganglion cell layer (GCL) were thinned 17.7 % [to 31.2 µm (±6.4 SD), p < 0.001] and 19.3 % [to 66.5 µm (±7.0 SD), p < 0.001] compared to the contralateral eye. Additionally, the ONL remained thickened at 96.7 µm (±7.0 SD, p < 0.001). At baseline, we found a significant correlation between the ONL thickness and the VF (r = -0.482, p = 0.005) and the BCVA at discharge (r = 0.552, p < 0.001), indicating that a thicker ONL correlates with poorer visual function. The GCL thickness also correlates with the BCVA at discharge (r = 0.411, p = 0.02), where a thinner GCL predicts worse BCVA. At the 1-month time point, the GCL thinning was correlated with both the VF (r = 0.471, p = 0.005) and the BCVA (r = -0.456, p = 0.007), indicating worse visual function. CONCLUSIONS: Changes in the thickness of different layers of the retina occur early in the course of AION and evolve over time, resulting in the atrophy of the GCL and RNFL. ONL thickening at baseline is associated with visual dysfunction. Thinning of the GCL after 1 month correlates with poorer VF and BCVA at 1 month after acute AION.

Loss of corneal sensory nerve fibers in SIV-infected macaques: an alternate approach to investigate HIV-induced PNS damage.

Peripheral neuropathy is the most frequent neurological complication of HIV infection, affecting more than one-third of infected patients, including patients treated with antiretroviral therapy. Although emerging noninvasive techniques for corneal nerve assessments are increasingly being used to diagnose and monitor peripheral neuropathies, corneal nerve alterations have not been characterized in HIV. Here, to determine whether SIV infection leads to corneal nerve fiber loss, we immunostained corneas for the nerve fiber marker ßIII tubulin. We developed and applied both manual and automated methods to measure nerves in the corneal subbasal plexus. These counting methods independently indicated significantly lower subbasal corneal nerve fiber density among SIV-infected animals that rapidly progressed to AIDS compared with slow progressors. Concomitant with decreased corneal nerve fiber density, rapid progressors had increased levels of SIV RNA and CD68-positive macrophages and expression of glial fibrillary acidic protein by glial satellite cells in the trigeminal ganglia, the location of the neuronal cell bodies of corneal sensory nerve fibers. In addition, corneal nerve fiber density was directly correlated with epidermal nerve fiber length. These findings indicate that corneal nerve assessment has great potential to diagnose and monitor HIV-induced peripheral neuropathy and to set the stage for introducing noninvasive techniques to measure corneal nerve fiber density in HIV clinical settings.

A unified graphical models framework for automated mitosis detection in human embryos.

Time lapse microscopy has emerged as an important modality for studying human embryo development, as mitosis events can provide insight into embryo health and fate. Mitosis detection can happen through tracking of embryonic cells (tracking based), or from low level image features and classifiers (tracking free). Tracking based approaches are challenged by high dimensional search space, weak features, outliers, missing data, multiple deformable targets, and weak motion model. Tracking free approaches are data driven and complement tracking based approaches. We pose mitosis detection as augmented simultaneous segmentation and classification in a conditional random field (CRF) framework that combines both approaches. It uses a rich set of discriminative features and their spatiotemporal context. It performs a dual pass approximate inference that addresses the high dimensionality of tracking and combines results from both components. For 312 clinical sequences we measured division events to within 30 min and observed an improvement of 25.6% and a 32.9% improvement over purely tracking based and tracking free approach respectively, and close to an order of magnitude over a traditional particle filter. While our work was motivated by human embryo development, it can be extended to other detection problems in image sequences of evolving cell populations.

Relationships between retinal axonal and neuronal measures and global central nervous system pathology in multiple sclerosis.

OBJECTIVE: To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN: Cross-sectional study. SETTING: Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS: A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES: High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS: Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer+inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P=.01 and P=.04, respectively) and caudate (P=.04 and P=.03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P=.007) and inversely associated with normal-appearing white matter volume (P=.005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning.

OBJECTIVE: To determine the effect of clinical and radiologic disease activity on the rate of thinning of the ganglion cell/inner plexiform (GCIP) layer and the retinal nerve fiber layer in patients with multiple sclerosis (MS) using optical coherence tomography (OCT). METHODS: One hundred sixty-four patients with MS and 59 healthy controls underwent spectral-domain OCT scans every 6 months for a mean follow-up period of 21.1 months. Baseline and annual contrast-enhanced brain MRIs were performed. Patients who developed optic neuritis during follow-up were excluded from analysis. RESULTS: Patients with the following features of disease activity during follow-up had faster rates of annualized GCIP thinning: relapses (42% faster, p = 0.007), new gadolinium-enhancing lesions (54% faster, p < 0.001), and new T2 lesions (36% faster, p = 0.02). Annual GCIP thinning was 37% faster in those with disability progression during follow-up, and 43% faster in those with disease duration <5 years vs >5 years (p = 0.003). Annual rates of GCIP thinning were highest in patients exhibiting combinations of new gadolinium-enhancing lesions, new T2 lesions, and disease duration <5 years (70% faster in patients with vs without all 3 characteristics, p < 0.001). CONCLUSIONS: MS patients with clinical and/or radiologic nonocular disease activity, particularly early in the disease course, exhibit accelerated GCIP thinning. Our findings suggest that retinal changes in MS reflect global CNS processes, and that OCT-derived GCIP thickness measures may have utility as an outcome measure for assessing neuroprotective agents, particularly in early, active MS.