A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography

Graßmann, Felix; Mengelkamp, Judith; Brandl, Caroline; Harsch, Sebastian; Zimmermann, Martina E.; Linkohr, Birgit; Peters, Annette; Heid, Iris M.; Palm, Christoph; Weber, Bernhard H. F.

doi:10.1016/j.ophtha.2018.02.037

Cited by 424 publications

(292 citation statements)

References 33 publications

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“…During the shared task, we hypothesized that an ensemble system that combines results of different methods could lead to better predictive performance than using a single method (19, 20). Hence, we addressed the CHEMPROT task using two ensemble systems that combined the results from three individual models, similar to our previous BioCreative submissions (21).…”

Section: Methodsmentioning

confidence: 99%

Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical–protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/

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Section: Methodsmentioning

confidence: 99%

Extracting chemical–protein relations with ensembles of SVM and deep learning models

et al. 2018

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“…Over the past decade, automated techniques for the assessment of AMD, via feature extraction from small retinal image datasets (<1000), have been reported with variable accuracy. More recent reports provide novel data on the accuracy of deep‐learning systems for the detection of AMD . The majority of these studies have utilized the Age‐Related Eye Disease Study (AREDS) participants to develop and test the accuracy of their DLAs .…”

Section: Discussionmentioning

confidence: 99%

“…Direct comparison between recent reports relating to AMD detection using DLAs is made difficult due to differing classification criteria. Burlina et al (2018), Grassman et al (2018) and Burlina et al (2017) utilized multi‐step approaches using classifications developed for AREDS. For example, Grassman et al defined 13 classes, one indicating little or no AMD, grades 2 to 9 representing changes associated with early or intermediate AMD and grades 10 to 12 covering late‐stage AMD such as geographic atrophy and neovascular AMD.…”

Section: Discussionmentioning

confidence: 99%

“…Burlina et al (2018), Grassman et al (2018) and Burlina et al (2017) utilized multi‐step approaches using classifications developed for AREDS. For example, Grassman et al defined 13 classes, one indicating little or no AMD, grades 2 to 9 representing changes associated with early or intermediate AMD and grades 10 to 12 covering late‐stage AMD such as geographic atrophy and neovascular AMD. Greater numbers of classification groups led to lower kappa scores in the study by Burlina et al (2018) (0.77 for four‐step approach and 0.74 in the nine‐step approach) and an overall accuracy of 63% in the study by Grassman et al (2018) .…”

Section: Discussionmentioning

confidence: 99%

“…For example, Grassman et al defined 13 classes, one indicating little or no AMD, grades 2 to 9 representing changes associated with early or intermediate AMD and grades 10 to 12 covering late‐stage AMD such as geographic atrophy and neovascular AMD. Greater numbers of classification groups led to lower kappa scores in the study by Burlina et al (2018) (0.77 for four‐step approach and 0.74 in the nine‐step approach) and an overall accuracy of 63% in the study by Grassman et al (2018) . Similarly, this was shown by Burlina et al (2017) who obtained accuracy values of 79.4%, 81.5% and 93.4% for 4‐class, 3‐class and 2‐class classifications, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs

Keel

Scheetz

et al. 2019

Clinical Exper Ophthalmology

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Importance Detection of early onset neovascular age‐related macular degeneration (AMD) is critical to protecting vision. Background To describe the development and validation of a deep‐learning algorithm (DLA) for the detection of neovascular age‐related macular degeneration. Design Development and validation of a DLA using retrospective datasets. Participants We developed and trained the DLA using 56 113 retinal images and an additional 86 162 images from an independent dataset to externally validate the DLA. All images were non‐stereoscopic and retrospectively collected. Methods The internal validation dataset was derived from real‐world clinical settings in China. Gold standard grading was assigned when consensus was reached by three individual ophthalmologists. The DLA classified 31 247 images as gradable and 24 866 as ungradable (poor quality or poor field definition). These ungradable images were used to create a classification model for image quality. Efficiency and diagnostic accuracy were tested using 86 162 images derived from the Melbourne Collaborative Cohort Study. Neovascular AMD and/or ungradable outcome in one or both eyes was considered referable. Main Outcome Measures Area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results In the internal validation dataset, the AUC, sensitivity and specificity of the DLA for neovascular AMD was 0.995, 96.7%, 96.4%, respectively. Testing against the independent external dataset achieved an AUC, sensitivity and specificity of 0.967, 100% and 93.4%, respectively. More than 60% of false positive cases displayed other macular pathologies. Amongst the false negative cases (internal validation dataset only), over half (57.2%) proved to be undetected detachment of the neurosensory retina or RPE layer. Conclusions and Relevance This DLA shows robust performance for the detection of neovascular AMD amongst retinal images from a multi‐ethnic sample and under different imaging protocols. Further research is warranted to investigate where this technology could be best utilized within screening and research settings.

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Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity

et al. 2022

JAMA Netw Open

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IMPORTANCERetinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Prediction of ROP before onset holds great promise for reducing the risk of blindness. OBJECTIVE To develop and validate a deep learning (DL) system to predict the occurrence and severity of ROP before 45 weeks' postmenstrual age. DESIGN, SETTING, AND PARTICIPANTS This retrospective prognostic study included 7033 retinal photographs of 725 infants in the training set and 763 retinal photographs of 90 infants in the external validation set, along with 46 characteristics for each infant. All images of both eyes from the same infant taken at the first screening were labeled according to the final diagnosis made between the first screening and 45 weeks' postmenstrual age. The DL system was developed using retinal photographs from the first ROP screening and clinical characteristics before or at the first screening in infants born between June 3, 2017, and August 28, 2019. EXPOSURES Two models were specifically designed for predictions of the occurrence (occurrence network [OC-Net]) and severity (severity network [SE-Net]) of ROP. Five-fold cross-validation was applied for internal validation. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance in ROP prediction. RESULTSThis study included 815 infants (450 [55.2%] boys) with mean birth weight of 1.91 kg (95% CI, 1.87-1.95 kg) and mean gestational age of 33.1 weeks (95% CI, 32.9-33.3 weeks).

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A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography

Abstract: Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.

Cited by 424 publications

References 33 publications

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Extracting chemical–protein relations with ensembles of SVM and deep learning models

Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs

Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity

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