The Role Of Artificial Intelligence In Ophthalmology
Artificial intelligence (AI) is a general term that means to accomplish a task mainly by a computer, with minimal human beings involved. In other words, the purpose of AI is to make computers mimic the way of our thinking, and improve our work efficiency in the modern fast-pace life.
It has become one of the most influential information technology revolutions. Great progress has been made in theoretical research and its application as far as we can see. AI is widely accepted as the appearance of many robots in difference fields, especially in bioinformatics.
Combined with medicine, some robot-assisted surgery has been conducted successfully. It makes doctor’s work more precisely and effectively. Nowadays, AI-assisted medical screening and diagnosis based on images are emerging.
As we all hear, melanoma, a skin cancer could be diagnosed with a computer algorithm based on macro images captured by a common camera. In the field of ophthalmology, especially in the blind-causing diseases, it mainly attributes to medical imaging identification and auxiliary diagnosis.
The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion.
Principle of Artificial Intelligence
The AI devices mainly fall into two major categories -the machine learning techniques and the natural language processing methods. But so far, the former is the auxiliary screening and diagnostic technique what we often talk about.
Machine learning provides techniques or algorithms that can automatically build a model of complex relationships by processing the input available data and generalizing a performance standard.
And it can be briefly described as enabling computers make successful predictions or judgments by repeatedly learning existing representative materials.
To be able to form an accurate model, machine learning often requires a large number of training data. And most of them need to be labeled its features in advance by relative authoritative experts.
Besides, some other data are used to verify the established algorithm. That means the processes mainly include two parts, training set and validation set. Therefore, an important step is to collect a lot of representative training examples.
Artificial intelligence (AI) software can perform cognitive functions like problem-solving and learning by processing and analyzing a large amount of data; in other words, the machine can gain experience as humans do.
It came into existence in 1956 and in no time spread its roots into many medical fields, including ophthalmology in the late 1990s when colour fundus photography had started gaining importance in diabetic retinopathy (DR) screening.
Later on, its use was not limited to but tried extensively in many subspecialties of the eye such as cataract, myopia and glaucoma screening, corneal ectasia, keratoconus, retinopathy of prematurity (ROP) and ocular reconstruction.
It can also be used in calculating intraocular lens power and while planning squint surgery and intravitreal injections. AI can even detect cognitive loss, Alzheimer’s disease and cerebrovascular stroke risk from fundus photographs and optical coherence tomography (OCT).
AI AND DR
The disease burden of diabetes mellitus increases day by day, and millions of people are affected. According to published data, the present disease burden is 463 million and likely to rise to 642 million by 2040.
DR is a microvascular complication affecting the retina’s blood vessels, leading to progressive damage and irreversible blindness. These patients need to be diagnosed early, and prompt treatment should be started regardless of the type of diabetes.
Routine dilated fundus screening in these patients with ophthalmoscopy and colour fundus photographs is the need of the hour and, therefore, eases the burden on the retina specialists. AI has shown promising results in the automated grading of DR based on ML and DL models, the CNN and the massive-training artificial neural network.
AI AND ROP
ROP is one of the leading causes of childhood blindness throughout the world. This vasoproliferative condition affects preterm infants with low gestational age and those with low birth weight. This condition should be diagnosed promptly so that timely intervention can be done.
This can be abetted with the help of AI, which provides an automated, quantifiable and highly objective diagnosis in plus disease in ROP.
One more area of application of AI in ROP is the utilization of the DL algorithms into medical training to standardize ROP training and education through tele-education. However, there are few clinical and technical challenges in the implementation of AI in the actual scenario.
AI AND AMD
AMD is considered the leading cause of central vision loss in the elderly age group. The challenges in diagnosing and managing this silent progressive retinal condition have led to the rising prevalence of the disease.
AI has evolved to help in the automated detection of drusens in the very early stages and stratify the disease’s progression. AMD is clinically characterized by the presence of drusens and retinal pigment epithelium changes progressing into geographic atrophy and neovasculari-zation.
AI AND GLAUCOMA
Glaucoma is a progressive optic neuropathy caused by high intra-ocular pressure leading to retinal nerve fibre loss and irreversible blindness. Early treatment can retard the progression of the disease. AI can help in identifying the borderline cases and predict the course of the disease.
Many studies have tried to apply ML to identify the disease. A comprehensive AI for glaucoma should be able to evaluate all the necessary parameters such as optic disc changes, intraocular pressure (IOP), gonioscopy, retinal nerve fiber layer thickness, visual fields etc.
However, such a comprehensive package is yet to come to the real-time world. The application of AI in measuring IOP is now limited to the Sensimed Triggerfish, a contact lens-based continuous IOP monitoring device that measures the corneal strain changes induced by IOP fluctuations.
AI AND CATARACT
Studies have described techniques to grade nuclear cataracts by the help of AI using algorithms based on ML or DL systems that work as efficiently as a clinician’s grading. Gao et al proposed a system that could process slit-lamp images to grade cataracts.
Liu et al focused on identifying and categorizing pediatric cataracts with excellent accuracy and sensitivity. Wu et al developed a universal AI platform and multilevel collaborative pattern that could perform effectively in diagnostic and referral service for pediatric and age-related cataracts.
Dong et al have proposed the automated detection and grading of cataracts from colour fundus photographs using a combination of a DL system to extract images (Caffe software) followed by a ML algorithm (called as Softmax function) for severity grading. AI has also been tried in residents’ cataract surgery training due to recognizing different phases of cataract surgery.
Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of artificial intelligence (AI) in medicine. Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues.
According to Konstantinos Balaskas, MD, FEBO, MRCOphth, a retinal expert at Moorfields Eye Hospital, London, United Kingdom, and director of the Moorfields Ophthalmic Reading Centre and AI Analytics Hub, AI is a broad term.
“The type of AI that has generated a lot of excitement in recent years is called ‘deep learning,’ ” he said. “This is a process by which software programs learn to perform certain tasks by processing large quantities of data.”
Deep learning is what has made ophthalmology a pioneer in the field of implementing AI in medicine, because we are increasingly reliant on imaging tests to monitor our patients.
“Particularly in my subspecialty of interest, medical retina, imaging tests such as optical coherence tomography (OCT) are performed very frequently and have provided the material to train, test, and then apply AI decision support systems,” Balaskas noted.
In retina particularly, some of the most common causes of visual loss in the Western world—such as age-related macular degeneration (AMD) and diabetic retinopathy—require early detection, prompt initiation of treatment, and regular monitoring to preserve vision.
Balaskas said this is where AI decision support systems can help improve access to care and ensure optimal clinical outcomes for patients. Balaskas cited the AI decision support system developed in collaboration between Moorfields Eye Hospital, where he is based, and Google DeepMind.
“It is able to read OCT scans, interpret them, provide a diagnosis, and make management recommendations,” he said. “The other area where AI shows promise is in the development of personalized treatment plans for patients by being able to predict their response to treatment and their visual outcomes over a period of time.”
When considering common conditions that threaten vision, such as AMD and diabetic retinopathy, Balaskas says AI decision support tools—once validated and once they have gained regulatory approval as medical devices—can help improve access to care.
“They can, for example, assist health practitioners in the community in diagnosing diseases early,” he explained.”
“In the United Kingdom, where OCT scans are widely available in high street optician practices, an AI tool would be particularly useful to assist them to interpret scans correctly and identify disease at an early stage.”
Similarly, in diabetic retinopathy, where patients require regular screening and monitoring, AI tools can significantly increase efficiency of screening programs. Balaskas pointed out that such applications already exist and can be of particular use in diabetic retinopathy screening programs such as in underresourced health care settings.
“Other indications for the application of AI monitoring, like AMD, are in advanced stages of development but have not yet been implemented in real life,” he added. Balaskas said there are challenges with integrating AI into retina diagnostics and treatments.
He noted that he has a personal academic interest in implementation science, which looks at the gap between developing a medical device such as an AI decision support tool and deploying it in clinical practice.
“The potential barriers that we need to overcome for the tool to be deployed in a meaningful way to improve outcomes for our patients go beyond testing and validation,” he said. “These include economic evaluations: how would such an automated decision support model affect the finances of a health care system, so that it could provide good value for money or achieve cost savings?”
The next consideration is human factors, particularly how these models of care that rely on AI are perceived and accepted by patients and practitioners. What is the level of trust in these technologies?
What level of information and education of patients and the public is required to build confidence in their use? Then there are considerations regarding training and technical infrastructure to support these tools.
Balaskas notes that ethical and data-privacy issues, as well as medicolegal considerations, are also important. Who is responsible for decisions made by an AI algorithm rather than a human? How do these tools affect the way health care professionals diagnose and treat disease?
“There is a phenomenon called automation bias, where practitioners are sometimes more likely to defer to the recommendation of the AI tool—even perhaps against their better judgement,” he said.
Balaskas notes the issue of interpretability— that in many instances these AI tools are opaque in their functioning. “We do not fully understand how a specific recommendation is reached, whether that is a diagnosis or a management recommendation, and that lack of transparency can exacerbate the medical, legal, and ethical issues that were mentioned earlier,” he pointed out.
“In summary, we have found that there are several hurdles to overcome before AI tools can be deployed in real life in a way that is safe and will improve clinical outcomes.” Moreover, Balaskas notes that life could change for ophthalmologists in the future, and he has a optimistic vision of AI in medical practice.
“Our field is becoming increasingly complex and we need to process data from various sources when we are assessing our patients: data from the many imaging modalities, genetic data and the various types of omics, such as proteomics and the emerging field of oculomics, where features on the eye examination can be indicative of problems with systemic health,” he said.
Balaskas also noted that data from home vision monitoring devices will become increasingly available. However, Balaskas noted that making sense of all this data in order to develop a personalized treatment plan for each patient can be daunting.
“AI could become a very useful aid and, as described in the Topol Review on AI commissioned by Health Education England, provide the gift of time to patients and practitioners, giving them the chance to discuss and decide together what the optimal treatment plan is, informed by the processing of high-dimensional complex data sources,” he concluded.