SNEC, SERI and NUS School of Computing Develop Artificial Intelligence To Screen For Three Major Eye Conditions


SINGAPORE NATIONAL EYE CENTRE (SNEC), SINGAPORE EYE RESEARCH INSTITUTE (SERI) AND NATIONAL UNVERSITY OF SINGAPORE (NUS) SCHOOL OF COMPUTING DEVELOP ARTIFICIAL INTELLIGENCE TO SCREEN FOR THREE MAJOR EYE CONDITIONS - DIABETIC RETINOPATHY, GLAUCOMA SUSPECT AND AGE-RELATED MACULAR DEGENERATION

Paper newly published in the Journal of American Medical Association (JAMA) 12 Dec 2017.

Newly developed AI system can screen for 3 eye conditions (first in the world):  Diabetic Retinopathy, Glaucoma Suspect (GS) and Age-related Macular Degeneration (AMD)

Introduction

Singapore National Eye Centre (SNEC) and Singapore Eye Research Institute (SERI) have partnered National University of Singapore (NUS) School of Computing to build an Artificial Intelligence (AI) system to screen for diabetic eye diseases, in collaboration with several leading eye centres globally (Australia, China, USA, Mexico and Hong Kong). This AI technology uses a Deep Learning System (DLS), the most novel machine learning technology, that thinks and makes decision like human intelligence in differentiating those with and without these conditions.

This study involved 30 co-investigators who, together, reported high diagnostic performance of an AI-based DLS in a screening program for diabetic patients in detecting Diabetic Retinopathy (DR), Glaucoma Suspect (GS) and Age-related Macular Degeneration (AMD) in multi-ethnic population with diabetes. This paper was newly published in the Journal of American Medical Association (JAMA) 12 Dec 2017 (attached).

The underlying technology is a DLS that has the ability to learn to identify and detect retinal images that show signs of DR and related eye diseases across multi-ethnic populations. 





Diabetic Retinopathy (DR) – Leading Cause of Blindness in Working Adults in Singapore

DR is the leading cause of preventable blindness among working adults in Singapore. One in three persons with diabetes have DR. A study showed that 5 of 6 people who have DR are unaware that they have the condition (Huang OS, Tay WT, Ong PG, et al. Br J Ophthalmol 2015; 99: 1614–1621).

With Singapore having an estimated 600,000 diabetics aged 18 to 69 (www.moh.gov.sg, 2011), about 180,000 have DR. The Ministry of Health in Singapore has declared ‘war on diabetes’ to rally the nation in an effort to reduce the burden of diabetes in our population and keep Singaporeans healthy as we age.

Currently, the most effective way to prevent DR-related vision loss is annual screening for DR, a universally accepted practice and recommended by American Diabetes Association and the International Council of Ophthalmology (ICO) to prevent vision loss. To address this problem, the Singapore Integrated Diabetic Retinopathy Program (SiDRP), was set up, and in 2017 screens 100,000 persons with diabetes across 18 primary care clinics in Singapore. However, SiDRP relies mostly on “human grading” of the retinal photographs by a large team of trained professional graders or optometrists. Given rising prevalence of diabetes, SiDRP and other DR screening programmes are challenged by availability, training and retention of professional graders and optometrists, long-term financial sustainability and access. DR screening remains patchy globally as a result of these challenges.

Using Artificial Intelligence (AI) and Deep Learning System (DLS) to Screen for DR

To address the challenges, the clinical team from SNEC and SERI (Professor Wong Tien Yin, Assistant Professor Daniel Ting) partnered with the technical team from NUS School of Computing (Professor Lee Mong Li Janice, Professor Wynne Hsu, Dr Gilbert Lim) to jointly develop an AI-based system that can screen retinal images. They are the co-inventors of this AI-based DLS, a new machine learning technology that uses representation-learning methods to process large data and recognise intricate structures and meaningful patterns that may not be visible to the human eye.

In this study, the researchers developed and trained the DLS to recognise and classify retinal images to detect DR, glaucoma suspects and age-related macular degeneration and compared it with the performance of human evaluators of the images in the SiDRP and other studies. This DLS was developed and tested using about 500,000 retinal images from multi-ethnic populations across different countries, including the SiDRP patients. The system had high rates of correctly identifying retinal images with and without diabetic retinopathy and related eye diseases.



“The DLS will be useful for aiding DR screening programmes in Singapore and elsewhere. In countries where there are existing programmes such as UK and Singapore, it will increase the efficiency and reduce cost of screening DR by replacing a large proportion of what is now requiring “human assessment”, said Professor Wong Tien Yin, senior author and Medical Director, SNEC, and Chairman, SERI.

“It will be easier to set up DR screening programmes in communities in the future which could largely be done automatically by DLS. It will also save cost and improve efficiency of healthcare system by allowing ophthalmologists and optometrists to concentrate on treating only DR cases that require treatment,” added Prof Wong who is also Chair of Ophthalmology & Vice-Dean, Duke-NUS Medical School, National University of Singapore

Collaborative Effort by SNEC-SERI with NUS School of Computing

“This system is a collaborative effort between the SNEC-SERI Clinical Team and the NUS School of Computing, which started many years ago. The paper reports on the use of AI and DLS for detecting three different retinal conditions - referable DR, GS and AMD.  The system has sensitivity greater than 90 per cent and specificity greater than 85 per cent to detect these conditions,” said Professor Lee Mong Li Janice, from the Department of Computer Science at the NUS School of Computing, who is also one of the study’s senior authors.

Fighting the ‘War on Diabetes’ and ‘Smart Nation’ Initiatives
“To date, this is the world’s first and largest dataset (with close to half a million of retinal images) evaluating the use of a DLS not only to screen for DR, but also other potentially vision-threatening eye conditions such as GS and AMD. In alignment with ‘War on Diabetes’ and the Smart Nation initiatives, we also hope to share our experience with other groups involved in AI-related research projects,” said Assistant Professor Dr Daniel Ting from the Singapore National Eye Center, SingHealth Duke-NUS, the lead author for the paper and the clinical lead of the team.
Dr Ting is also awarded the highly prestigious 2017 US-ASEAN Visiting Fulbright Scholar Program by the US Government, representing Singapore to visit Johns Hopkins University to share and exchange his domain expertise in the field of AI and Medicine. “AI is deemed to be the 4th industrial revolution in the human history. In healthcare, we need to embrace this technology earlier to improve work efficiency, while maintaining the high standard of clinical care. Although this is an exciting result, there is still a lot more areas in AI that we need to research further,” he added.

Next Steps

The team is now beta testing the AI system in the Singapore Diabetic Retinopathy Screening Programme (SiDRP) alongside human graders. They are also increasing datasets from around the world, aiming to achieve five million images over the next five years.

“We are also developing more complex algorithms for different DR severity levels, predictive algorithms for DR incidence and progression, diabetes-related systemic complications for e.g. stroke, coronary diseases and chronic kidney diseases,” said Professor Wynne Hsu, from the Department of Computer Science at the NUS School of Computing, who is also one of the senior co-authors of the paper.

Left picture: A patient with referable Diabetic Retinopathy (DR)
Right picture (same image): The heat map attention generated by the Artificial Intelligence (AI) system, showing the areas characterized by the presence of diabetes eye-related changes. This image is diagnosed as referable DR.



The study authors would like to thank the National Medical Research Council (NMRC) and
The Tanoto Foundation for the grant funding support 



Annex A
Useful Questions and Answers
(by Prof Wong Tien Yin)

1) What is deep learning system, and how does it detect diabetic retinopathy and related eye diseases using retinal images?
Deep learning system (DLS) is an artificial intelligence (AI)-based machine learning technology that uses methods to process large quantities of data in their raw form, recognising intricate structures and patterns that may not be visible to the human eyes. DLS uses convoluted neural networks (CNN) as their “brain” to learn and train. Using this DLS, target-specific features are automatically learnt by CNNs in the feature extraction stage and then fed into a classifier for classification. The DLS approach does not involve any objective judgement and the feature extraction process is entirely automatic, so that features that are neither noticed by humans previously nor examined before will also be assessed.
In this study, we developed and trained a DLS to classify retinal image into those with and without referable diabetic retinopathy, referable glaucoma suspects and age-related macular degeneration (AMD) and once this DLS was trained, we tested and validated the DLS to detect these eye conditions using separate datasets, including 10 external datasets from multi-ethnic populations across different countries.
2) How might this research influence patient care? What has to happen before it may reach the clinic?

The DLS will be useful for aiding DR screening programmes. In countries where there are existing programmes (e.g., UK, Singapore), it will increase the efficiency and reduce cost of screening diabetic retinopathy by replacing a large proportion of what is now requiring “human assessment”.

In communities and countries without existing programmes and without sufficient ophthalmologists (e.g., developing countries, parts of China, India, South America), it can be used as a first line screening tool to accurately screen for cases of that require referral to an ophthalmologist for treatment.

We are now beta testing this in the Singapore national screening programme alongside human assessors so once the comparison is adequate, it will be implemented




3) What is the background for this study? What are the main findings?

Currently, annual screening for  DR is a universally accepted practice and recommended by American Diabetes Association and the International Council of Ophthalmology (ICO) to prevent vision loss. However, implementation of DR screening programs across the world require human assessors (ophthalmologists, optometrists or professional technicians trained to read retinal photographs). Such screening programmes are thus challenged by issues related to a need for significant human resources and long-term financial sustainability.

To address these challenges, we developed an AI-based software using a deep learning, a new machine learning technology. This DLS  utilises representation-learning methods to process large data and extract meaningful patterns. In our study, we developed and validated this DLS using about 500,000 retinal images in a “real world screening programme” and 10 external datasets from global populations.

The results suggest excellent accuracy of the DLS with sensitivity of 90.5 per cent and specificity of 91.6 per cent, for detecting referable levels of DR and 100 per cent sensitivity and 91.1 per cent specificity for vision-threatening levels of DR (which require urgent referral and should not be missed). In addition, the performance of the DLS was also high for detecting referable glaucoma suspects and referable age-related macular degeneration (which also require referral if detected).

The DLS was tested in 10 external datasets comprising different ethnic groups: Caucasian whites, African-Americans, Hispanics, Chinese, Indians and Malaysians.

4) What are the takeaways from your research?

First, it will be easier to set up DR screening programmes in communities in the future which could largely be done automatically by DLS.

Second, it will save cost and improve efficiency of healthcare systems by allowing ophthalmologists and optometrists to concentrate on treating only DR cases that require treatment.

5) What are the recommendations for future research as a result of this study?

We are now beta testing this in the Singapore national screening programme alongside human assessors so once the comparison is adequate, it will be implemented.



For the LATEST tech updates,
FOLLOW us on our Twitter
LIKE us on our FaceBook
SUBSCRIBE to us on our YouTube Channel!
SHARE
    Blogger Comment
    Facebook Comment

0 comments: