Cecilia S. Lee

Wide-field optical coherence tomography based microangiography for retinal imaging

Optical coherence tomography angiography (OCTA) allows for the evaluation of functional retinal vascular networks without a need for contrast dyes. For sophisticated monitoring and diagnosis of retinal diseases, OCTA capable of providing wide-field and high definition images of retinal vasculature in a single image is desirable. We report OCTA with motion tracking through an auxiliary real-time line scan ophthalmoscope that is clinically feasible to image functional retinal vasculature in patients, with a coverage of more than 60 degrees of retina while still maintaining high definition and resolution. We demonstrate six illustrative cases with unprecedented details of vascular involvement in retinal diseases. In each case, OCTA yields images of the normal and diseased microvasculature at all levels of the retina, with higher resolution than observed with fluorescein angiography. Wide-field OCTA technology will be an important next step in augmenting the utility of OCT technology in clinical practice.

Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images

Objective The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Design EMR and OCT database study. Subjects Normal and AMD patients who had a macular OCT. Methods Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure Area under the ROC. Results Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.Purpose The advent of electronic medical records (EMRs) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most common imaging modality in ophthalmology and represents a dense and rich data set when combined with labels derived from the EMR. We sought to determine whether deep learning could be utilized to distinguish normal OCT images from images from patients with age-related macular degeneration (AMD). Design EMR and OCT database study. Subjects Normal and AMD patients who underwent macular OCT. Methods Automated extraction of an OCT database was performed and linked to clinical end points from the EMR. Optical coherence tomography scans of the macula were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical end points extracted from EPIC. The central 11 images were selected from each OCT scan of 2 cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operating characteristic (ROC) curves were constructed at an independent image level, macular OCT level, and patient level. Main Outcome Measure Area under the ROC curve. Results Of a recent extraction of 2.6 million OCT images linked to clinical data points from the EMR, 52 690 normal macular OCT images and 48 312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC curve of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC curve of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC curve of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69%, respectively. Conclusions The deep learning technique achieves high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer-aided diagnosis tools in the future.

IgG4-associated orbital and ocular inflammation

BackgroundIgG4-associated orbital and ocular inflammation is a relatively unknown entity characterized by sclerosing inflammation with infiltration of IgG4-positive plasma cells. Some so-called idiopathic inflammation syndromes are being re-classified as IgG4-associated inflammation with histopathologic evaluation.FindingsWe report three cases with differing manifestations of IgG4-associated ocular and orbital inflammation: a case of recurrent, treatment-refractory sclero-uveitis that was diagnosed as granulomatosis with polyangiitis with an IgG4-related component, a case of pachymeningitis with optic neuritis that resulted in permanent visual loss, and a case of orbital inflammatory pseudotumor. All three would have been incompletely diagnosed without thorough histopathologic evaluation (including immunohistochemistry).ConclusionsIgG4-associated disease is an idiopathic, multi-organ inflammatory state that can manifest as chronic, relapsing, sclerosing inflammation in virtually any organ system. There is a wide range of presentations in ocular and orbital inflammation. Ophthalmologists should keep IgG4-associated inflammation in mind when examining chronic, sclerofibrosing inflammation with multi-system involvement. The histology of biopsy specimens is crucial in making the correct diagnosis. Timely assessment may lead to fewer diagnostic tests and more targeted therapy.

UK AMD EMR USERS GROUP REPORT V: benefits of initiating ranibizumab therapy for neovascular AMD in eyes with vision better than 6/12.

Background/aims To study the effectiveness and clinical relevance of eyes treated with good (better than 6/12 or >70 Early Treatment Diabetic Retinopathy Study letters) visual acuity (VA) when initiating treatment with ranibizumab for neovascular age-related macular degeneration (nAMD) in the UK National Health Service. Currently eyes with VA better than (>) 6/12 are not routinely funded for therapy. Methods Multicentre national nAMD database study on patients treated 3–5 years prior to the analysis. Anonymised structured data were collected from 14 centres. The primary outcome was the mean VA at year 1, 2 and 3. Secondary measures included the number of clinic visits and injections. Results The study included 12 951 treatment-naive eyes of 11 135 patients receiving 92 976 ranibizumab treatment episodes. A total of 754 patients had baseline VA better than 6/12 and at least 1-year of follow up. Mean VA of first treated eyes with baseline VA>6/12 at year 1, 2, 3 were 6/10, 6/12, 6/15, respectively and those with baseline VA 6/12 to >6/24 were 6/15, 6/17, 6/20, respectively (p values <0.001 for comparing differences between 6/12 and 6/12–6/24 groups). For the second eyes with baseline VA>6/12, mean VA at year 1, 2, 3 were 6/9, 6/9, 6/10 and those with baseline VA 6/12 to >6/24 were 6/15, 6/15, 6/27, respectively (p values <0.001–0.005). There was no significant difference in the average number of clinic visits or injections between those with VA better and worse than 6/12. Conclusions All eyes with baseline VA>6/12 maintained better mean VA than the eyes with baseline VA 6/12 to >6/24 at all time points for at least 2 years. The significantly better visual outcome in patients who were treated with good baseline VA has implications on future policy regarding the treatment criteria for nAMD patients’ funding.

Paucibacterial microbiome and resident DNA virome of the healthy conjunctiva

Purpose To characterize the ocular surface microbiome of healthy volunteers using a combination of microbial culture and high-throughput DNA sequencing techniques. Methods Conjunctival swab samples from 107 healthy volunteers were analyzed by bacterial culture, 16S rDNA gene deep sequencing (n = 89), and biome representational in silico karyotyping (BRiSK; n = 80). Swab samples of the facial skin (n = 42), buccal mucosa (n = 50), and environmental controls (n = 27) were processed in parallel. 16S rDNA gene quantitative PCR was used to calculate the bacterial load in each site. Bacteria were characterized by site using principal coordinate analysis of metagenomics data. BRiSK data were analyzed for presence of fungi and viruses. Results Corynebacteria, Propionibacteria, and coagulase-negative Staphylococci were the predominant organisms identified by all three techniques. Quantitative 16S PCR demonstrated approximately 0.1 bacterial 16S rDNA/human actin copy on the ocular surface compared with greater than 10 16S rDNA/human actin copy for facial skin or the buccal mucosa. The conjunctival bacterial community structure is distinct compared with the facial skin (R = 0.474, analysis of similarities P = 0.0001), the buccal mucosa (R = 0.893, P = 0.0001), and environmental control samples (R = 0.536, P = 0.0001). 16S metagenomics revealed substantially more bacterial diversity on the ocular surface than other techniques, which appears to be artifactual. BRiSK revealed presence of torque teno virus (TTV) on the healthy ocular surface, which was confirmed by direct PCR to be present in 65% of all conjunctiva samples tested. Conclusions Relative to adjacent skin or other mucosa, healthy ocular surface microbiome is paucibacterial. Its flora are distinct from adjacent skin. Torque teno virus is a frequent constituent of the ocular surface microbiome. (ClinicalTrials.gov number, NCT02298881.)

Anti-tubercular therapy for intraocular tuberculosis: A systematic review and meta-analysis.

Intraocular tuberculosis remains a diagnostic and management conundrum for both ophthalmologists and pulmonologists. We analyze the efficacy and safety of anti-tubercular therapy (ATT) in patients with intraocular tuberculosis and factors associated with favorable outcome. Twenty-eight studies are included in this review, with a total of 1,917 patients. Nonrecurrence of inflammation was observed in pooled estimate of 84% of ATT-treated patients (95% CI 79-89). There was minimal difference in the outcome between patients treated with ATT alone (85% successful outcome; 95% CI 25-100) and those with concomitant systemic corticosteroid (82%; 95% CI 73-90). The use of ATT may be of benefit to patients with suspected intraocular tuberculosis; however, this conclusion is limited by the lack of control group analysis and standardized recruitment and treatment protocols. We propose further prospective studies to better establish the efficacy of ATT and ascertain the factors associated with favorable treatment outcomes.

Deep-learning based, automated segmentation of macular edema in optical coherence tomography

Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations of clinically relevant image features.

Viral posterior uveitis

The causes of posterior uveitis can be divided into infectious, autoimmune, or masquerade syndromes. Viral infections, a significant cause of sight-threatening ocular diseases in the posterior segment, include human herpesviruses, measles, rubella, and arboviruses such as dengue, West Nile, and chikungunya virus. Viral posterior uveitis may occur as an isolated ocular disease in congenital or acquired infections or as part of a systemic viral illness. Many viruses remain latent in the infected host with a risk of reactivation that depends on various factors, including virulence and host immunity, age, and comorbidities. Although some viral illnesses are self-limiting and have a good visual prognosis, others, such as cytomegalovirus retinitis or acute retinal necrosis, may result in serious complications and profound vision loss. Since some of these infections may respond well to antiviral therapy, it is important to work up all cases of posterior uveitis to rule out an infectious etiology. We review the clinical features, diagnostic tools, treatment regimens, and long-term outcomes for each of these viral posterior uveitides.

Emerging techniques for pathogen discovery in endophthalmitis.

Purpose of review Despite the inability to detect certain organisms and relatively low yield, microbial culture is the current gold standard for the diagnosis of most intraocular infections. Research on alternative molecular diagnostic methods has produced an array of strategies that augment and improve pathogen detection. This review summarizes the most recent literature on this topic. Recent findings The yield of traditional microbial culture has not improved since the Endophthalmitis Vitrectomy Study results were published 20 years ago. Advances in PCR methods have enabled quantification of pathogen load and screening for multiple organisms at once. More recently, deep sequencing techniques allow highly sensitive detection of any DNA-based life form in a specimen. This offers the promise of not only improved detection of traditional organisms but can also identify organisms not previously associated with endophthalmitis. Summary Molecular diagnostic methods enhance the results of microbial culture and may become the new standard in the diagnosis of intraocular infections.

Projection Artifact Removal Improves Visualization and Quantitation of Macular Neovascularization Imaged by Optical Coherence Tomography Angiography

PURPOSE To visualize and quantify the size and vessel density of macular neovascularization (MNV) using optical coherence tomography angiography (OCTA) with a projection artifact removal algorithm. DESIGN Multicenter, observational study. PARTICIPANTS Subjects with MNV in at least one eye. METHODS Patients were imaged using either a swept-source OCT angiography (SS-OCTA) prototype system or a spectral-domain OCT angiography (SD-OCTA) prototype system. The optical microangiography (OMAG) algorithm was used to generate the OCTA images. Projection artifacts from the overlying retinal circulation were removed from the OMAG OCTA images using a novel algorithm. Following removal of the projection artifacts from the OCTA images, we assessed the size and vascularity of the MNV. Concurrent fluorescein angiography (FA) and indocyanine green angiography (ICGA) images were used to validate the artifact-free OMAG images whenever available. MAIN OUTCOME MEASURES Size and vascularity of MNV imaged with OCTA before and after the use of a projection-artifact removal algorithm. RESULTS A total of 30 subjects (40 eyes) diagnosed with MNV were imaged. Five patients were imaged before and after intravitreal injections of vascular endothelial growth factor (VEGF) inhibitors. Following the use of the projection artifact removal algorithm, we found improved visualization of the MNV. Lesion sizes and vascular densities were more easily measured on all the artifact-free OMAG images. In eyes treated with vascular endothelial growth factor inhibitors, vascular density was reduced in all five eyes after treatment, and in four eyes, the size of the MNV decreased. One of five patients showed a slight increase in lesion size, but a decrease in vascular density. CONCLUSIONS OCTA imaging of MNV using the OMAG algorithm combined with removal of projection artifacts resulted in improved visualization and measurements of the neovascular lesions. OMAG with projection artifact removal should be useful for assessing the response of MNV to treatment using OCTA imaging.