Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration.

Importance: Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability. Objective: To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy. Design, Setting, and Participants: This study used data collected from November 13, 1992, to November 30, 2005, from 4613 study participants of the AREDS data set to develop deep convolutional neural networks that were trained to provide detailed automated AMD grading on several AMD severity classification scales, using a multiclass classification setting. Two AMD severity classification problems using criteria based on 4-step (AMD-1, AMD-2, AMD-3, and AMD-4 from classifications developed for AREDS eligibility criteria) and 9-step (from AREDS detailed severity scale) AMD severity scales were investigated. The performance of these algorithms was compared with a contemporary human grader and against a criterion standard (fundus photograph reading center graders) used at the time of AREDS enrollment and follow-up. Three methods for estimating 5-year risk were developed, including one based on DL regression. Data were analyzed from December 1, 2017, through April 15, 2018. Main Outcomes and Measures: Weighted κ scores and mean unsigned errors for estimating 5-year risk probability of progression to advanced AMD. Results: This study used 67 401 color fundus images from the 4613 study participants. The weighted κ scores were 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The overall mean estimation error for the 5-year risk ranged from 3.5% to 5.3%. Conclusions and Relevance: These findings suggest that DL AMD grading has, for the 4-step classification evaluation, performance comparable with that of humans and achieves promising results for providing AMD detailed severity grading (9-step classification), which normally requires highly trained graders, and for estimating 5-year risk of progression to advanced AMD. Use of DL has the potential to assist physicians in longitudinal care for individualized, detailed risk assessment as well as clinical studies of disease progression during treatment or as public screening or monitoring worldwide.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

Importance: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. Objective: To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Design, Setting, and Participants: Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Main Outcomes and Measures: Accuracy, receiver operating characteristics and area under the curve, and kappa score. Results: The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Conclusions and Relevance: Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

BACKGROUND: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. METHODS: Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. RESULTS: Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-, 3-, 2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. DISCUSSION: This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading.

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images.

BACKGROUND: Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the "wet" form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the "dry" form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intra-observer variability. METHODS: We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist. RESULTS: Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07. DISCUSSION: This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These preliminary results show promising evidence that machine learning methods may have utility in automated characterization of GA from color fundus images.

Automated classification of severity of age-related macular degeneration from fundus photographs.

PURPOSE: To evaluate an automated analysis of retinal fundus photographs to detect and classify severity of age-related macular degeneration compared with grading by the Age-Related Eye Disease Study (AREDS) protocol. METHODS: Following approval by the Johns Hopkins University School of Medicine's Institution Review Board, digitized images (downloaded AT http://www.ncbi.nlm.nih.gov/gap/) of field 2 (macular) fundus photographs from AREDS obtained over a 12-year longitudinal study were classified automatically using a visual words method to compare with severity by expert graders. RESULTS: Sensitivities and specificities, respectively, of automated imaging, when compared with expert fundus grading of 468 patients and 2145 fundus images are: 98.6% and 96.3% when classifying categories 1 and 2 versus categories 3 and 4; 96.1% and 96.1% when classifying categories 1 and 2 versus category 3; 98.6% and 95.7% when classifying category 1 versus category 3; and 96.0% and 94.7% when classifying category 1 versus categories 3 and 4; CONCLUSIONS: Development of an automated analysis for classification of age-related macular degeneration from digitized fundus photographs has high sensitivity and specificity when compared with expert graders and may have a role in screening or monitoring.

Structure and properties of collagen vitrigel membranes for ocular repair and regeneration applications.

The frequency of ocular injuries on the battlefield has been steadily increasing during recent conflicts. Combat-related eye injuries are difficult to treat and solutions requiring donor tissue are not ideal and are often not readily available. Collagen vitrigels have previously been developed for corneal reconstruction, but increased transparency and mechanical strength are desired for improved vision and ease of handling. In this study, by systematically varying vitrification temperature, relative humidity and time, the collagen vitrigel synthesis conditions were optimized to yield the best combination of high transparency and high mechanical strength. Optical, mechanical, and thermal properties were characterized for each set of conditions to evaluate the effects of the vitrification parameters on material properties. Changes in denaturing temperature and collagen fibril morphology were evaluated to correlate properties with structure. Collagen vitrigels with transmittance up to 90%, tensile strength up to 12 MPa, and denaturing temperatures that significantly exceed the eye/body temperature have been synthesized at 40 °C and 40% relative humidity for one week. This optimal set of conditions enabled improvements of 100% in tensile strength and 11% in transmittance, compared to the previously developed collagen vitrigels.

Light-scattering and ultrastructure of healed penetrating corneal wounds.

PURPOSE: To investigate quantitatively for the first time the relationship between light-scattering and ultrastructure of semitransparent scars resulting from penetrating wounds in rabbit cornea. METHODS: Penetrating wounds, 2 mm in diameter, were made in the central cornea and allowed to heal for 3.6 to 4.5 years at which time the rabbits were killed. The scar and cornea thickness outside the scar were measured using ultrasonic pachymetry. Corneas were excised immediately and their transmissivity was measured from 400 to 700 nm. The tissue was then prepared for transmission electron microscopy. Transmission electron micrographs (TEMs) were analyzed to determine fibril positions and radii. Scattering was calculated using the direct summation of fields (DSF) METHOD: RESULTS: Scar thickness averaged 0.26 +/- 0.04 mm, and the scars were flat. Thickness outside the scars averaged 0.40 +/- 0.04 mm. Three scars were moderately transparent, five were less transparent, and one was much less transparent. The wavelength dependence of the measured total scattering cross- section was indicative of the presence of voids (lakes) in the collagen fibril distribution, and lakes were evident in the TEMs. The images showed enlarged fibrils and some showed bimodal distributions of fibril diameters. Calculated scattering was characteristic of that expected from regions containing lakes-a finding consistent with the scattering measurements. CONCLUSIONS: Despite the long healing time, these scars remained highly scattering. A combination of lakes, disordered fibril distributions, and a significant population of enlarged fibrils can explain the scattering. A possible cellular contribution cannot be ruled out.