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Tech giants lead eyecare innovation

Tech giants are spending billions researching eyecare in a novel approach to ensure consumers can enjoy mobile devices and other visual technologies for their entire lives. SANDEEP BARDIA reports.

It doesn’t take a rocket scientist to deduce that eyecare is of direct interest to any company selling devices for viewing media, such as smartphones, computers, tablets and more. Certainly, the increased penetration of such technologies in day-to-day life is exacerbating strain on the eyes and forcing companies such as Microsoft, IBM and Google to research eyecare.

But, do these companies have the right mix of technology and industry experience to achieve successful outcomes?

Quite a few tech companies have tried to formulate the right set of participants in their consortiums by collaborating with healthcare professionals, technology players in the ophthalmology industry and researchers.

These players are creating a win-win situation for everyone by bringing together the best possible talent in the field of eyecare; however, having the right technology and partnerships is not enough to make this pursuit a success. Other factors will also need to be tackled to make this venture successful in the long run.

So far, many companies are moving in the right direction. Microsoft India has launched Microsoft Intelligence Network for Eyecare (MINE), a new research group in collaboration with the LV Prasad Eye Institute, a leading eyecare hospital in India.

Other partner institutions joining this global consortium include Bascom Palmer – University of Miami, Flaum Eye Institute – University of Rochester (USA), the Federal University of Sao Paulo (Brazil), and the Brien Holden Vision Institute.

"In specific areas like medical imaging, you can see we’re going to make really tremendous progress in the next couple of years with artificial intelligence."
Dominic King, Clinical lead for DeepMind Health

The focus of this consortium is to integrate machine learning to identify conditions leading to blindness. Microsoft will be utilising its Cortana Intelligence Suite, a leading cloud-based platform, to conduct advanced analytics as well as to create artificial intelligence models on eyecare.

Microsoft’s research is especially focused on children and the company is striving to develop early interventions for preventing eye diseases in the future.

Google has a different approach compared to Microsoft. Unlike Microsoft’s global ambitions, Google’s ophthalmology research is focused on the UK market. Google’s DeepMind division has collaborated with UK’s Moorfields Eye Hospital NHS Foundation to conduct research in the areas of macular degeneration and diabetic retinopathy.

Recently, the collaboration announced a breakthrough, with the Financial Times reporting that DeepMind had trained an algorithm to spot signs of eye disease more effectively than human specialists. The researchers did this with the use of data from thousands of 3D retinal scans that had been painstakingly labelled for signs of disease by ophthalmologists.

“In specific areas like medical imaging, you can see we’re going to make really tremendous progress in the next couple of years with artificial intelligence,” Dr Dominic King, clinical lead for DeepMind Health, told the Financial Times.

“Machine learning could have a very important role picking up things more sensitively and specifically than currently happens.”

The algorithm was able to utilise the millions of pixels of information contained within the scans and use it to search for signs of glaucoma, diabetic retinopathy and AMD. The company has now begun negotiations regarding clinical trials with hospitals, including Moorfields.

Meanwhile, according to King, as the AI is “generalised”, it may also be applied to other kinds of images – such as radiotherapy scans – in the future.

Another company that has successfully used AI machine learning to scan retinal images for signs of eye disease is IBM. The tech multinational’s Watson division, part of which is based in Melbourne, has been making news in fields ranging from healthcare, to retail and financial services.

IBM's Watson supercomputer

Last year, it announced the expansion of its Watson Health Medical Imaging Collaborative, increasing the number of participating organisations to 24. Members include leading health systems, academic medical centres, imaging companies and ambulatory radiology providers.

The focus of this collaboration is to integrate cognitive imaging of a wide range of health conditions, including eye diseases, into the daw y-to-day practices of doctors. It will aim to do this with the use of its supercomputer, Watson, which combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a ‘question answering’ machine.

By applying deep learning techniques and image analytics technology to hundreds of thousands of de-identified retina images accessed through EyePACS, the researchers were able to train Watson to first analyse key anomalies of the eye to recognise signs of DR, and then classify the degree of severity with 86% accuracy.

These emerging computer vision methods that identify and classify lesions in an image within 20 seconds could create new levels of efficiency and help clinicians screen a greater number of diabetes patients, and quickly refer those who need specialist care.

“Recent advancements in deep learning and image analytics technologies are showing significant promise in the potential to help solve some of the greatest health challenges we face today,” Dr Joanna Batstone, vice president and lab director of IBM Research Australia says.

“Automated and highly accurate DR screening methods have the potential to help doctors screen far more patients than currently possible.”


As Watson can help researchers draw insight from a huge volume of structured and unstructured data sets in a way never before possible, it has the potential to transform the ways we diagnose, treat and monitor various healthcare conditions.

Leading ophthalmic device manufacturer Topcon has joined the initiative to accelerate the development of technologies to understand, diagnose and treat eye conditions.

IBM is also helping Bausch and Lomb to create a clinical calculation app for cataract surgeons by partnering with Apple.

How IBM's deep learning program highlights the haemorrhages

The app will make surgeons’ tasks easier and the entire surgery process more efficient by integrating all patient data, records of previous surgeries and other calculation data, along with IBM’s cognitive capabilities.

The focus areas and the target markets of all these tech companies may be different, but the underlying idea or technology is essentially the same – to harness the power of AI and machine learning to analyse millions of data points that will assist in the development of analytics-backed intervention and diagnostic models.

In doing so, the hope is that it will allow us to detect eye diseases or conditions leading to blindness at an earlier stage.

These companies have invested billions into artificial intelligence and machine learning and are now lending their technology to help eyecare researchers and healthcare professionals scale up their R&D efforts, analysis and data-crunching capabilities.

But what are the future implications of all this development?

First of all, new technologies will not only create early-intervention and diagnosis models that significantly reduce the eye health complexities that lead to blindness, but they will also enable eyecare practitioners to offer more personalised-treatment options to patients by leveraging the data-crunching and analytical capabilities of these technology tools.

Secondly, eyecare will become much more affordable with the help of these new approaches, especially in emerging and developing countries.

However, the path towards success is neither easy nor short. Both Google and Microsoft are relying on digital eye scans for conducting their research. Computer-aided diagnosis is not completely novel and lots of work has been done in this area but, complex eye scans have not been fully explored by the existing analytical tools until now.

"Deep learning and image analytics technologies are showing ... The potential to help solve some of the greatest health challenges we face."
Joanna Batstone, Vice president and lab director of IBM Research Australia

Several research groups in the past have made an effort to develop algorithms for the automated diagnosis of eye conditions; however, such efforts have been debated due to the use of limited datasets and the testing of these algorithms against human interpreters. So, given the challenges in the past, why should the industry get excited about these new developments?

The technological advancements made by tech companies such as IBM’s Watson or Google’s DeepMind significantly enhance the computing capabilities and performance of the hardware and software used in such analysis.

Google, Microsoft, Apple and others still need to successfully apply these technologies on a commercial scale, but the industry can be confident that significant improvements in the data-analytics and processing capabilities of tools like IBM Watson and DeepMind’s Neural Network are helping to develop diagnostic capabilities by analysing millions of data points with ease and efficiency.

Advanced Neural Networks will overcome the limitations posed by algorithms developed earlier by mimicking the functions of the human brain as closely as possible and yielding new analytical capabilities to eyecare diagnostic models.

The tech giants have the necessary infrastructure, technology and finances to overcome several of the challenges faced by researchers in the past, such as the high cost of R&D, limited technological capabilities, and a lack of data sets to conduct their work. However, challenges still exist concerning the reliability and sensitivity of diagnoses offered by these new technologies.

Further improvements in analytical models and the use of larger data sets will eventually help technology to outshine human experts in both reliability and sensitivity. Once successful, these advancements are expected to improve patient safety and access to eyecare services.

The key to the success for these players is to build larger data sets and strike the right partnerships with eyecare organisations across the globe. IBM, Microsoft and Google are already progressing at a rapid rate and healthcare professionals must understand that these technologies are not intending to replace them, but help them by making their daily tasks easier and more efficient.

Tech giants are on the right path in their mission to make future generations healthier. Even though their intrinsic motivation may be to create healthier customers for their products in the future, their outcomes cannot be questioned.

Expanding the healthcare applications of new inventions will not only help tech companies achieve strong returns on their investments, but also help them fulfil their corporate social responsibilities by contributing towards the betterment of human health.

Written by Sandeep Bardia

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