Artificial Intelligence: The Future for Glaucoma Diagnosis

Artificial intelligence (AI), also known as machine intelligence, refers to the capability of a machine to imitate human intelligence. It is now ubiquitous in everyday life, from the automated tagging of faces on Facebook to driverless cars. In the very near future, AI may also assist in the early diagnosis of glaucoma.

The rise of AI has been exponential over the past five years and follows advances in computer power, data volume, and theoretical understanding. AI techniques are now an essential part of the technology industry.

As electronic medical records become mainstream and digital image acquisition and data storage evolve, we will have more opportunities to access clinical data and patient images than ever before. An emerging area of diagnostic imaging in ophthalmology involves the use of automated computer vision image interpretation with deep learning algorithms. This involves training the algorithm on large datasets of well labelled images so that it can learn the features from the data itself rather than from predefined rules.

Sets of algorithms inspired by neural activity in the human brain, called ‘neural networks’, are designed to recognise patterns such as vertical lines in an image. Neural networks allow data to pass through layers of algorithms in a multistep process of pattern recognition to produce an output.¹ Each step in deep learning allows the program to continually learn and evaluate its progress to reach a specific outcome.² This technique lends itself particularly to fields that are highly image driven and therefore has not been limited to use in ophthalmology but has also been applied to dermatology and radiology.

AI And Glaucoma

Glaucoma is the leading cause of irreversible blindness worldwide.^3,4,5 It is currently estimated that 64.3 million individuals are affected by this disease in the world,⁵ and this number is anticipated to reach 112 million by 2040.⁵ Blindness in glaucoma can be avoidable if the disease is detected early enough and treatment strategies commenced.^6-8 However, ≥50 per cent of cases with glaucoma are undiagnosed in most population surveys around the world.^9-14 This is attributable to glaucoma being largely asymptomatic until late in the disease process.

Glaucoma diagnosis is dependent on assessment of the optic disc and the surrounding retinal nerve fibre layer by a trained specialist. Therefore glaucoma screening has always been limited in its cost-effectiveness due to the skills required and its labour intensive nature.^15,16 Given the growing public health concern, improvements for screening and diagnosis of glaucoma are warranted. AI, through the use of automated computer vision image interpretation with deep learning algorithms, offers a novel method of diagnostic imaging.

Different research groups around the world have analysed machine learning models for the diagnosis of glaucoma using available data such as visual fields, optical coherence tomography (OCT), and optic disc characteristics, and demonstrated excellent sensitivity, specificity, and area under the receiver operative characteristic curve (AUC) (Table1).^17-29 Researchers at the Centre for Eye Research Australia (CERA) have formed a collaboration with the technology company Healgoo Interactive Med-Tech (Guangzhou, China) to develop a deep learning system based on an image data pool of close to 50,000 images collected from a range of clinical settings in China. A gold standard grading of each of these images was assigned based on the consensus reached by three individual glaucoma specialists. The deep learning algorithm was validated against an independent sample of 8,000 images and achieved an overall diagnostic accuracy in excess of 90 per cent (sensitivity = 95.6 per cent, specificity = 92.0 per cent) for the classification of referable glaucomatous optic neuropathy from standard colour 45° fundus photographs.²¹ 

Although still in its relative infancy in the specialty of glaucoma, the United States Food and Drug Administration (FDA) has already approved the first AI system for use in screening for diabetic retinopathy.³⁰ We believe that, before long, the same will be seen for glaucoma.

However, the complexity of glaucoma diagnosis poses some unique challenges for AI systems that differ from retinal disease. In particular, population differences in fundus pigmentation, disc size, and cup to disc ratio are a potential source of bias for these systems. Tilted and myopic discs can also be problematic. These challenges can potentially be overcome with a diverse training dataset. Furthermore, while current deep learning algorithms rely on training using single modality imaging such as fundus photography, and subjective grading by specialists to label images (that can have its own limitations), the future is likely to see improved accuracy through the addition of real world clinical data. Optical coherence tomography and automated visual fields, for example, will improve automated glaucoma classification. Ultimately, given glaucoma progression and treatment response can vary markedly across patients, we would like to see this leading on to improved risk prediction and personalised treatment for individual patients. It is anticipated that the use of longitudinal data may assist in classifying cases into predictive outcomes and determine likelihood of treatment response.

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