Decoding the Challenges and Opportunities in Artificial Intelligence

Decoding the Challenges and Opportunities in

Artificial Intelligence by Brooke Herron

AI algorithms can be as good as ophthalmologists and retinal specialists in detecting DR.

Recent developments in machine learning and artificial intelligence (AI) have created a buzz throughout the ophthalmic industry. Most notably, is the use of deep learning to screen and detect diseases like diabetic retinopathy (DR), diabetic macular edema (DME), glaucoma and others. For busy surgeons, this extra hand in patient diagnosis can not only save valuable time, it could also potentially increase access to eye care for millions of patients.

Now, as the technology grows closer to fruition, health care providers must learn and decide how to implement and integrate AI into their practice. At the recent 78 th Annual Conference of the All India Ophthalmological Society (AIOC 2020), industry experts dove into this topic, revealing their experience and vital input into this critically important technology.

AI in Ophthalmology

So, what exactly do all these buzzwords mean? In her presentation, Dr. Naama Hammel, a clinical research scientist at Google Health, succinctly defined the three main terms: (1) artificial intelligence (AI) is a grand project [for machines] to build non-human intelligence; (2) machine learning (ML) occurs when machines learn to be smarter [by analyzing data]; and (3) deep learning (DL) is a particular kind of machine learning [often associated with human-like artificial intelligence].

According to Dr. Hammel, there are several reasons to apply DL to medicine. “Deep learning is very useful in situations where we have a lot of data to look through and we don’t have enough expertise to do it – and this is exactly the situation in diabetic retinopathy,” she explained. “Worldwide, there are 415 million people with diabetes. That’s a lot of data to look through, plus expertise in grading can be limited — this is where an algorithm can scale and enhance.”

This is important because all of diabetic patients need an annual screening for DR — and there aren’t enough doctors to check, or even screen, all of these people. And even in teleophthalmology DR screening programs, there aren’t enough doctors to grade all the acquired photos. This is precisely where AI can fill the gap by reading fundus images to detect DR. So, how accurate is AI in screening DR? Dr. Hammel discussed a study published in 2016 where Google collaborated with two big hospitals in India. Together, they trained a model that can read a new fundus photo and determine whether the patient has DR. “The algorithm turned out to be very accurate… on par with ophthalmologists,” she stated. “Additional work on the same data improved the algorithm and it’s now on par with retina specialists, who are the gold standard.”

She continued that the algorithm will only be as good as we teach it; so, if we train it to detect DR, that’s what it will do. If it sees something else, it will simply say ‘no DR’. “This is something that’s very important, because it’s different from what humans think. They [algorithms] are trained to do a specific task and that’s what they know how to do.”

Not to fret, AI will aid — not replace— doctors.

understand machine learning: “We need to be ready for the prime time when it comes,” said Dr. Hammel.

Screening and Detection

By the year 2040, the global burden of patients will diabetes is expected to rise to 642 million. For ophthalmologists, that mean that of those, an estimated 224 million will have some form of DR; while 70 million will have sight-threatening DR. India alone has 77 million cases of diabetes, with approximately 15 million patients affected with DR. With the rising incidence of diabetes and its comorbidities, more effective screening and diagnosis will certainly be needed.

Unfortunately, in India and across the developing world, there is often a delay in detecting DR – and this is precisely where AI can step in. Traditionally, access to appropriate specialists or imaging devices could cause a delay in DR detection. However today, there is an opportunity to treat more patients using teleophthalmology (especially in rural areas) and AI. In her presentation discussing AI in DR detection, Dr. R. Rajalakshmi, from Dr. Mohan’s Diabetes Specialities Centre, explained that machines are taught to recognize specific patterns in high-resolution images — in fact, the system uses thousands of retinal images of varying grades of DR, which allow it to make an accurate diagnosis.

Additionally, using AI for DR detection has been proven to be fast, consistent, scalable and accurate. According to Dr. Rajalakshmi, there are several advantages to this approach: Physicians are less burdened and can screen large numbers of patients, leading to earlier detection and enabling specialists to focus on treatment. Plus, many AI systems have an accuracy of more than 90%, making them a reliable diagnostic.

However, as with anything, AI does have limitations. “A machine may miss things that a human reader would notice, unless it is trained to identify the lesion, giving patients a false sense of security,” shared Dr. Rajalaksmhi.

Editor’s Note:

AIOC 2020 was held in Gurugram, India, from February 13 to 16, 2020. Reporting for this story also took place at AIOC 2020. Media MICE Pte Ltd, PIE Magazine’s parent company, was the media partner at AIOC 2020.

Current Challenges in AI

Of course, when incorporating any new technology into an existing practice, there are challenges to overcome — and adding AI into the diagnostic mix is no different. Dr. Rajalakshmi said that currently, the challenge is deploying these models into clinical practice, and it must happen with care and caution. Governmental and regulatory approval for the use of AI in medicine is in various stages worldwide.

She concluded that ophthalmologists should not feel threatened by AI: “AI is not the ‘AlphaGo’ that will eventually replace the ophthalmologist,” she said. “It is a tool — not a threat.”