A team of researchers has developed a system using artificial intelligence (AI) that can identify the genetic cause of inherited retinal diseases from eye scans.
Inherited retinal diseases (IRDs) – single-gene disorders affecting the retina – are very difficult to diagnose as they are uncommon and involve changes in one of many candidate genes. Outside specialist centres, there are few experts who know enough about these diseases, which makes it difficult for patients to access proper testing and diagnosis.
Lead researcher Dr Nikolas Pontikos at the University College London (UCL) Institute of Ophthalmology and Moorfields Eye Hospital in London and his team have developed the Eye2Gene algorithm, which they hope will lead to more efficient and widespread testing.
He spoke about his findings at the annual conference of the European Society of Human Genetics in Glasgow (June 10th).
"Identifying the causative gene from a retinal scan is considered extremely challenging, even by experts. However, the AI is able to achieve this to a higher level of accuracy than most human experts," said Dr Pontikos.
It's estimated that 1 in 2000 people have inherited retinal diseases, which are the leading cause of vision loss in patients between the ages of 15 and 45 years old.
Vast Database of Retinal and Genetic Information
The researchers in the UK, with colleagues in Germany, used Moorfield's huge database of information on IRDs. More than 4000 patients received a genetic diagnosis as well as detailed retinal imaging at the eye hospital, making it the largest single centre dataset of patients with both retinal and genetic data.
Identification of the gene involved in a retinal disease is often guided by using the patient's phenotype defined using the Human Phenotype Ontology (HPO).
The HPO involves the use of standardised and structured descriptions of medical terms of a patient's phenotype, which are observable characteristics of an individual resulting from the expression of genes, to allow scientists and doctors to communicate more effectively.
"However, HPO terms are often imperfect descriptions of retinal imaging phenotypes, and the promise of Eye2Gene is that it can provide a much richer source of information than HPO terms alone, by working directly from the retinal imaging," explained Dr Pontikos.
The team benchmarked Eye2Gene on 130 IRD cases with a known gene diagnosis for which whole exome/genome, retinal scans, and detailed HPO descriptions were available, and compared their HPO gene scores with the Eye2Gene gene scores. They found Eye2Gene provided a rank for the correct gene higher or equal to the HPO-only score in over 70% of cases.
Dr Pontikos said: "In the future, Eye2Gene could be easily incorporated into standard retinal examination, first as an assistant in specialist hospitals in order to get a second opinion, and eventually as a synthetic expert where such a person is not available. Ideally, Eye2Gene software would be embedded into the retinal imaging device."
Regulatory Approval Still Needed
The system will need to go through regulatory approvals to demonstrate safety and efficacy before its use becomes more widespread.
"We need further evaluation of Eye2Gene in order to assess its performance for different types of IRD patients from different ethnicities, different types of imaging devices, and in different types of settings, for example primary vs secondary care. Clinical trials will be required before our system can be deployed in clinics as medical device software," said Dr Pontikos.
He continued: "We all know that a picture is worth a thousand words, so we had some expectation that retinal scans interpreted by AI could out-perform HPO terms only. But we were still pleasantly surprised to see that, even when quite specific HPO terms were used, Eye2Gene could still do as well or better than an HPO-only approach. We hope that AI will help patients and their families by making specialist care more efficient, accessible, and equitable."
Professor Alexandre Reymond, chair of the conference, said: "While real life experts are essential, the use of AI will help in mitigating biases and will allow diagnoses for all in the future."
Retina UK's chief executive, Tina Garvey, commented: "Precise diagnosis can make a huge difference to affected families, and we are delighted that Dr Pontikos and his team are harnessing the potential of AI to make this accessible to everyone."