Use of AI in eye scans helps improve diagnosis of inherited disease of the retina – Times of India
Artificial intelligence (AI) could help save countless patients from blindness, claims a new study. A team of researchers said that they have utilised artificial intelligence (AI) to develop an eye scan that can help improve diagnosis of inherited disease of the retina (IRDs). The retina is a small membrane at the back of the eye that contains light-sensitive cells. These single-gene disorders affecting the retina are said to be very difficult to diagnose since they are uncommon and involve changes in one of many candidate genes.
Dr Nikolas Pontikos, a group leader at the University College London’s Institute of Ophthalmology and Moorfields Eye Hospital, and the team developed Eye2Gene, an AI system capable of identifying the genetic cause of IRDs from retinal scans.
“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 in a media release.
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 is can provide a much richer source of information than HPO terms alone by working directly from the retinal imaging,” said 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 per cent of cases.
In the future, Eye2Gene could be easily incorporated into standard retinal examinations. “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,” the team noted.
The findings were shared at the annual conference of the European Society of Human Genetics recently.
Dr Nikolas Pontikos, a group leader at the University College London’s Institute of Ophthalmology and Moorfields Eye Hospital, and the team developed Eye2Gene, an AI system capable of identifying the genetic cause of IRDs from retinal scans.
“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 in a media release.
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 is can provide a much richer source of information than HPO terms alone by working directly from the retinal imaging,” said 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 per cent of cases.
In the future, Eye2Gene could be easily incorporated into standard retinal examinations. “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,” the team noted.
The findings were shared at the annual conference of the European Society of Human Genetics recently.
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