Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

By Daniel Shu Wei Ting, C. Cheung, Gilbert Lim, G. Tan, N. Quang, A. Gan, H. Hamzah, R. García-Franco, Ian Yew San Yeo, S. Lee and 19 more. 2017

2,075 citations recorded by the source corpus.

Subfields: Health Information Management, Ophthalmology, Radiology, Nuclear Medicine and Imaging, Medical Imaging, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Ramesh Raskar
2. Tristan Swedish
3. Praneeth Vepakomma

In Ophthalmology

1. Emily Y. Chew
2. Daniel Shu Wei Ting
3. Jan M. Köhler

In Radiology, Nuclear Medicine and Imaging

1. Praneeth Vepakomma
2. Tristan Swedish
3. Dinggang Shen
4. Ramesh Raskar
5. Emily Y. Chew
6. Daniel Shu Wei Ting
7. Dale R. Webster
8. Jan M. Köhler

In Medical Imaging

1. Dinggang Shen

In Medical & Health AI

1. Heimo Müller
2. Kurt Zatloukal
3. Georg Langs
4. H. Denk
5. Dinggang Shen
6. L. Rodney Long
7. Sameer Kiran Antani
8. Daniel Shu Wei Ting

Citations in the corpus

• Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, background): “Background of the disease: As people with diabetes have a high prevalence for RDR [47], a frequent retinal screening is recommended and deep learning algorithms have been successfully developed to classify fundus images ([8], [20], [3], [46]).”
• Causability and explainability of artificial intelligence in medicine (2019, background): “A further example is the promising results of identifying diabetic retinopathy and related eye diseases (Ting et al., 2017).”
• The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, methodology): “Aside from those regarding data privacy and safety listed previously, the main concern is accountability.(9,28) Particularly in healthcare, poor decisions carry heavy consequences and the current paradigm is that some person must be held accountable.”
• Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy (2017, background): “…highly accurate algorithms for a variety of clas-sification tasks, including melanoma [11], breast cancer lymph node metastasis [12,13], and DR [14–16] Because the network is trained to predict labels that have been paired with the images, it is imperative that the labels accurately represent the…”
• DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs (2018, background): “19,21,27,32,33 In general, deep learning is the process of training algorithmic models with labeled data (e.g., color fundus photographs categorized manually as containing pigmentary abnormalities or not), where these models can then be used to assign labels automatically to new data.”
• Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence (2018, background): “[18] done in Singapore with multiple retinal images taken with conventional fundus cameras from multi-ethnic cohorts of people with diabetes, their algorithm showed a high sensitivity and specificity for”