Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.
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ABSTRACT:
SUBMITTER: Yan Q
PROVIDER: S-EPMC7153739 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
Publications
Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.
Yan Qi Q Weeks Daniel E DE Xin Hongyi H Swaroop Anand A Chew Emily Y EY Huang Heng H Ding Ying Y Chen Wei W
Nature machine intelligence 20200214 2
Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by fundus images and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have utilized both genetic and image data for predicting AMD progression. Here we jointly used genotypes and fundus images to predict an eye as having progressed to late AMD w ...[more]
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Project description:By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2-86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1-81.5) and 82.0 (81.8-82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.
| S-EPMC7453007 | biostudies-literature
Project description:PurposeBecause age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches.MethodsGenotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches-neural network, lasso regression, support vector machine, and random forest-were implemented. A standard logistic regression model using a genetic risk score was also considered.ResultsAll machine learning-based methods achieved satisfactory performance for predicting advanced AMD cases (vs. normal controls) (area under the curve = 0.81-0.82, Brier score = 0.17-0.18 in a separate test dataset) and any stage AMD (vs. normal controls) (area under the curve = 0.78-0.79, Brier score = 0.18-0.20 in a separate test dataset). The prediction performance was further validated in an independent dataset of 783 subjects from UK Biobank (area under the curve = 0.67).ConclusionsBy applying multiple state-of-art machine learning approaches on large AMD genome-wide association studies datasets, the predictive models we established can provide an accurate estimation of an individual's AMD risk profile based on genetic information along with age. The online prediction interface is available at: https://yanq.shinyapps.io/no_vs_amd_NN/.Translational relevanceThe accurate and individualized risk prediction model interface will greatly improve early diagnosis and enhance tailored clinical management of AMD.
| S-EPMC7900884 | biostudies-literature
Project description:Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2-5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.
| S-EPMC8617775 | biostudies-literature
Project description:We propose a hybrid sequential prediction model called "Deep Sequence", integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.
| S-EPMC7508843 | biostudies-literature
Project description:PurposeNascent geographic atrophy (nGA) refers to specific features seen on OCT B-scans, which are strongly associated with the future development of geographic atrophy (GA). This study sought to develop a deep learning model to screen OCT B-scans for nGA that warrant further manual review (an artificial intelligence [AI]-assisted approach), and to determine the extent of reduction in OCT B-scan load requiring manual review while maintaining near-perfect nGA detection performance.DesignDevelopment and evaluation of a deep learning model.ParticipantsOne thousand eight hundred and eighty four OCT volume scans (49 B-scans per volume) without neovascular age-related macular degeneration from 280 eyes of 140 participants with bilateral large drusen at baseline, seen at 6-monthly intervals up to a 36-month period (from which 40 eyes developed nGA).MethodsOCT volume and B-scans were labeled for the presence of nGA. Their presence at the volume scan level provided the ground truth for training a deep learning model to identify OCT B-scans that potentially showed nGA requiring manual review. Using a threshold that provided a sensitivity of 0.99, the B-scans identified were assigned the ground truth label with the AI-assisted approach. The performance of this approach for detecting nGA across all visits, or at the visit of nGA onset, was evaluated using fivefold cross-validation.Main outcome measuresSensitivity for detecting nGA, and proportion of OCT B-scans requiring manual review.ResultsThe AI-assisted approach (utilizing outputs from the deep learning model to guide manual review) had a sensitivity of 0.97 (95% confidence interval [CI] = 0.93-1.00) and 0.95 (95% CI = 0.87-1.00) for detecting nGA across all visits and at the visit of nGA onset, respectively, when requiring manual review of only 2.7% and 1.9% of selected OCT B-scans, respectively.ConclusionsA deep learning model could be used to enable near-perfect detection of nGA onset while reducing the number of OCT B-scans requiring manual review by over 50-fold. This AI-assisted approach shows promise for substantially reducing the current burden of manual review of OCT B-scans to detect this crucial feature that portends future development of GA.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
| S-EPMC10818248 | biostudies-literature
Project description:PURPOSE:Age-related macular degeneration (AMD) is a degenerative disease of the macula, often leading to progressive vision loss. The rate of disease progression can vary among individuals and has been associated with multiple risk factors. In this review, we provide an overview of the current literature investigating phenotypic, demographic, environmental, genetic, and molecular risk factors, and propose the most consistently identified risk factors for disease progression in AMD based on these studies. Finally, we describe the potential use of these risk factors for personalised healthcare. RECENT FINDINGS:While phenotypic risk factors such as drusen and pigment abnormalities become more important to predict disease progression during the course of the disease, demographic, environmental, genetic and molecular risk factors are more valuable at earlier disease stages. Demographic and environmental risk factors such as age and smoking are consistently reported to be related to disease progression, while other factors such as sex, body mass index (BMI) and education are less often associated. Of all known AMD variants, variants that are most consistently reported with disease progression are rs10922109 and rs570618 in CFH, rs116503776 in C2/CFB/SKIV2L, rs3750846 in ARMS2/HTRA1 and rs2230199 in C3. However, it seems likely that other AMD variants also contribute to disease progression but to a lesser extent. Rare variants have probably a large effect on disease progression in highly affected families. Furthermore, current prediction models do not include molecular risk factors, while these factors can be measured accurately in the blood. Possible promising molecular risk factors are High-Density Lipoprotein Cholesterol (HDL-C), Docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), zeaxanthin and lutein. SUMMARY:Phenotypic, demographic, environmental, genetic and molecular risk factors can be combined in prediction models to predict disease progression, but the selection of the proper risk factors for personalised risk prediction will differ among individuals and is dependent on their current disease stage. Future prediction models should include a wider set of genetic variants to determine the genetic risk more accurately, and rare variants should be taken into account in highly affected families. In addition, adding molecular factors in prediction models may lead to preventive strategies and personalised advice.
| S-EPMC7155063 | biostudies-literature
Project description:PurposeTo evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); and to discuss the technique of artificial intelligence/deep learning to potentially enhance research in detecting and classifying AMD.DesignRetrospective literature review.MethodsTo review the studies of both prospective and retrospective (post hoc) analyses of nutrition, genetic variants, and deep learning in AMD in both the Age-Related Eye Disease Study (AREDS) and AREDS2.ResultsIn addition to demonstrating the beneficial effects of the AREDS and AREDS2 supplements of antioxidant vitamins and zinc (plus copper) for reducing the risk of progression to late AMD, these 2 studies also confirmed the importance of high adherence to Mediterranean diet in reducing progression of AMD in persons with varying severity of disease. In persons with the protective genetic alleles of complement factor H (CFH), the Mediterranean diet had further beneficial effect. However, despite the genetic association with AMD progression, prediction models found genetic information added little to the high predictive value of baseline severity of AMD for disease progression. The technique of deep learning, an arm of artificial intelligence, using color fundus photographs from AREDS/AREDS2 was superior in some cases and noninferior in others to clinical human grading (retinal specialists) and to the gold standard of the certified reading center graders.ConclusionsCounseling individuals affected with AMD regarding the use of the AREDS2 supplements and the beneficial association of the Mediterranean diet is an important public health message. Although genetic testing is important in research, it is not recommended for prediction of disease or to guide therapies and/or dietary interventions in AMD. Techniques in deep learning hold great promise, but further prospective research is required to validate the use of this technique to provide improvement in accuracy and sensitivity/specificity in clinical research and medical management of patients with AMD.
| S-EPMC8324084 | biostudies-literature
Project description:To describe baseline night vision symptoms and their association with >/=3-lines loss in visual acuity (VA), choroidal neovascularization (CNV), and geographic atrophy (GA).Cohort study within a multicenter randomized clinical trial.A total of 1052 participants with >/=10 large (>125 mu) drusen and VA >/=20/40 in each eye.At baseline, participants self-administered a 10-item Night Vision Questionnaire (NVQ-10). VA testing was performed at baseline, 6 months, and annually. One eye of each participant was randomly assigned to laser treatment, and the contralateral eye was assigned to observation. During follow-up, trained readers identified CNV on the basis of fluorescein angiograms and end point GA, defined as >1 disc area of new GA, based on color photographs. Evaluation was performed by repeated-measures logistic regression for NVQ-10 score as a risk factor for >/=3-lines loss in VA and by survival analysis for CNV and GA, with and without adjustment for participant and ocular characteristics. Evaluations were based on observed eyes and treated eyes, considered separately and combined.A >/=3-lines loss in VA, development of CNV and end point GA.At baseline, NVQ-10 scores ranged from 3 to 100 with a mean of 70 (100 corresponds to no night vision symptoms). Compared with participants with the best night vision (fourth quartile of scores), participants with the worst night vision (first quartile of scores) were at increased risk of >/=3-lines loss in VA in both observed and treated eyes; odds ratios (95% confidence interval) were 2.85 (1.85-4.39) and 2.00 (1.27-3.14), respectively. The relative risk for the first quartile versus the fourth quartile for development of GA was 4.18 (1.80-9.68) in observed eyes and 2.59 (1.13-5.95) in treated eyes. The relative risk for CNV incidence was 1.99 (1.12-3.54) in observed eyes and 1.33 (0.81-2.19) in treated eyes. These relationships were maintained after adjustment for baseline participant and ocular characteristics.Participants who perceived the most problems in their night vision at baseline had an increased risk of >/=3-lines loss in VA, CNV, and GA. These associations are independent of established risk factors.
| S-EPMC2600589 | biostudies-literature
Project description:PurposeTimely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.DesignCross sectional study.ParticipantsFive hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.MethodsThe diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.Main outcome measuresThe area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.ResultsFor the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.ConclusionsDeep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.Financial disclosuresProprietary or commercial disclosure may be found after the references.
| S-EPMC9791595 | biostudies-literature
Project description:Spatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called "microperimetry") constitute laborious examinations. We have devised a machine-learning-based approach to predict mesopic and dark-adapted (DA) retinal sensitivity in eyes with neovascular age-related macular degeneration (nAMD). Extensive psychophysical testing and volumetric multimodal retinal imaging data were acquired including mesopic, DA red and DA cyan FCP, spectral-domain optical coherence tomography and confocal scanning laser ophthalmoscopy infrared reflectance and fundus autofluorescence imaging. With patient-wise leave-one-out cross-validation, we have been able to achieve prediction accuracies of (mean absolute error, MAE [95% CI]) 3.94 dB [3.38, 4.5] for mesopic, 4.93 dB [4.59, 5.27] for DA cyan and 4.02 dB [3.63, 4.42] for DA red testing. Partial addition of patient-specific sensitivity data decreased the cross-validated MAE to 2.8 dB [2.51, 3.09], 3.71 dB [3.46, 3.96], and 2.85 dB [2.62, 3.08]. The most important predictive feature was outer nuclear layer thickness. This artificial intelligence-based analysis strategy, termed "inferred sensitivity", herein, enables to estimate differential effects of retinal structural abnormalities on cone- and rod-function in nAMD, and may be used as quasi-functional surrogate endpoint in future clinical trials.
| S-EPMC6668439 | biostudies-literature