An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Li, Zhixi; Keel, Stuart; Liu, Chi; He, Yifan; Meng, Wei; Scheetz, Jane; Lee, Pei Ying; Shaw, Jonathan E.; Ting, Daniel Shu Wei; Wong, Tien Yin; Taylor, Hugh R.; Chang, Robert; He, Miao

doi:10.2337/dc18-0147

Cited by 205 publications

(152 citation statements)

References 35 publications

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“…The results in the DR‐AMD dataset show the system is able to differentiate between the two diseases, which is one of the main aspects in joint detection, since the presence of other pathologies may affect the performance of a DL system when detecting a given disease (Li et al. ). When identifying referable DR, false‐positive detections corresponded to 9.4% of the cases graded as non‐referable DR in the reference standard, which can be divided into controls or referable AMD cases, being the latter the 17.4% of the cases wrongly classified as referable DR.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

González-Gonzalo

Sánchez-Gutiérrez

Hernández-Martínez

et al. 2019

Acta Ophthalmologica

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Purpose: To validate the performance of a commercially available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and agerelated macular degeneration (AMD) in colour fundus (CF) images on a dataset with mixed presence of eye diseases. Methods: Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD by an experienced ophthalmologist to establish the reference standard (RS), and by four independent observers for comparison with human performance. Validation was furtherly assessed on Messidor (1200 images) for individual identification of referable DR, and the Age-Related Eye Disease Study (AREDS) dataset (133 821 images) for referable AMD, against the corresponding RS. Results: Regarding joint validation on the DR-AMD dataset, the system achieved an area under the ROC curve (AUC) of 95.1% for detection of referable DR (SE = 90.1%, SP = 90.6%). For referable AMD, the AUC was 94.9% (SE = 91.8%, SP = 87.5%). Average human performance for DR was SE = 61.5% and SP = 97.8%; for AMD, SE = 76.5% and SP = 96.1%. Regarding detection of referable DR in Messidor, AUC was 97.5% (SE = 92.0%, SP = 92.1%); for referable AMD in AREDS, AUC was 92.7% (SE = 85.8%, SP = 86.0%). Conclusion: The validated system performs comparably to human experts at simultaneous detection of DR and AMD. This shows that DL systems can facilitate access to joint screening of eye diseases and become a quick and reliable support for ophthalmological experts.

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Section: Discussionmentioning

confidence: 99%

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

González-Gonzalo

Sánchez-Gutiérrez

Hernández-Martínez

et al. 2019

Acta Ophthalmologica

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“…Ting et al [14] validated their DLS using 494,661 retinal images, demonstrating the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases, for the detection of any DR (AUC = 0.94-0.96); for possible glaucoma, AUC was 0.942; for AMD, AUC was 0.931. Similarly, Li et al [15] describe the development and validation of an artificial intelligence-based in 71,043 retinal images acquired from a web-based, deep learning algorithm for the detection of referable diabetic retinopathy. Testing against the independent multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%.…”

Section: Discussionmentioning

confidence: 99%

Detecting abnormal fundus images by employing deep transfer learning

Chen

et al. 2019

Preprint

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BackgroundTo develop and validate a deep transfer learning (DTL) algorithm in detecting abnormalities of fundus images from non-mydriatic fundus photography examination.Methods1,295 fundus images from January 2017 to December 2018 at Yijishan Hospital of Wannan Medical College were collected for developing and validating the deep transfer learning algorithm in detecting abnormal fundus images. The DTL model was developed by using 929(normal 254, abnormal 402) fundus images, including normal fundus images and abnormal fundus images, the latter including, maculopathy, optic neuropathy, vascular lesion, choroidal lesions, vitreous disease, cataract and the others. We tested our model using a subset of the publically available MESSIDOR dataset (using 366 images) and evaluate the testing performance of the DTL model for detecting abnormal fundus images. ResultsIn the internal validation data set (n=273 images), the AUC, sensitivity, accuracy and specificity of the DTL for correctly classified funds images were 0.997, 97.41%, 97.07% and 96.82%, respectively. For test data set (n=273 images), the AUC, sensitivity, accuracy and specificity of the DTL for correctly classification funds images were 0.926, 88.17%, 87.18% and 86.67%, respectively.ConclusionIn the evaluation, the DTL presented high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of the DTL in the community health care center.

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“…Technology offers new possibilities for policy evaluation in conjunction with the aforementioned classical approaches. Artificial intelligence tools are already widely used in the field of healthcare in areas such as the prediction and management of depression, voice recognition for people with speech impediments, the detection of changes in the biopsychosocial status of patients with multiple morbidities, stress control, the treatment of phantom limb pain, smoking cessation, personalized nutrition by prediction of glycaemic response, to try to detect signs of depression and in particular for reading medical images [2][3][4][5][6]. The generation of data implies a huge potential for the impact assessment of these interventions with new analytical tools.…”

Section: Introductionmentioning

confidence: 99%

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning

Seguí

Aguilar

Maeztu³

et al. 2020

IJERPH

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Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables “avoiding the need of a face-to-face visit” and “increased demand” (precision = 0.98 and 0.97, respectively) rather than the variable “type of query” (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

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An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Cited by 205 publications

References 35 publications

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

Detecting abnormal fundus images by employing deep transfer learning

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning

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