Artificial intelligence (AI) is at an inflection point in health care. A 50-year span of algorithm and software development has produced some powerful approaches to extracting patterns from big data. For example, deep-learning neural networks have been shown to be effective for image analysis, resulting in the first FDA-approved AI-aided diagnosis of an eye disease called diabetic retinopathy, using only photos of a patient's eye. However, the application of AI in the health care domain has also revealed many of its weaknesses, outlined in a recent guidance document from the World Health Organization (WHO). The document covers a lengthy list of topics, each of which are just as important as the last: responsible, accountable, inclusive, equitable, ethical, unbiased, responsive, sustainable, transparent, trustworthy and explainable AI.
You are free to share this article under the Attribution 4.0 International license. New biomarkers in the eyes could help manage diabetic retinopathy, and perhaps even diabetes, according to new research. During its early stages, diabetes can affect the eyes before the changes are detectable with a regular clinical examination. New research shows these changes can be measured earlier than previously thought with specialized optical techniques and computer analysis. The ability to detect biomarkers for this sight-threatening condition may lead to the early identification of people at risk for diabetes or visual impairment, as well as improve physicians' ability to manage these patients.
SAN FRANCISCO--July 28, 2021-- The American College of Radiology Data Science Institute (ACR DSI) and the American Academy of Ophthalmology today announced a collaboration that will expand ACR DSI's groundbreaking AI-LAB platform to include eye care. Leveraging use cases and data from the Academy, this collaboration will accelerate the use of machine learning in the ophthalmic industry to the benefit of patients across the globe. "We've now made it easier for the ophthalmology community to access real world examples for our own use cases. By working together with ACR, we are leveraging a platform developed for the radiology community to educate our own community about AI development and encouraging new AI to be developed that will benefit our specialty," said Tamara R. Fountain, MD, president of the American Academy of Ophthalmology. The Academy will provide the ophthalmology content and the ACR will provide the IT infrastructure to integrate the use cases and datasets into the landmark AI-LAB.
Ocular diseases are extensively-studied in the healthcare world as they affect millions of people. With this in mind, we decided to build an ML model in PerceptiLabs that applies image recognition techniques on fundus images to detect possible cataracts in patients. Using a model like this could help doctors, optometrists, and researchers to more easily classify and detect such conditions. To train our model, we grabbed the Ocular Disease Recognition dataset on Kaggle that comprises fundus images representing seven ocular-related conditions and well as normal images (i.e., those depicting no-ocular-related conditions). For our use case, we narrowed down the dataset to 293 images representing normal images and 293 representing cataracts.
Deep learning is seeing tremendous adoption in different industries. One specific area where deep learning has shown great potential is Computer Vision. I personally graduated from a computer vision master's program and went immediately to work in the industry. So what follows is my take on different trends that I am seeing in companies that are using deep learning to tackle challenging computer vision problems. So going back to my studies, in the middle of the master's program, I did an internship in a company in Luxembourg that makes large scanners of wood!
Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that its EyeArt AI system for diabetic eye testing has been chosen for deployment in 4 hospitals in Binh Dinh Province, Vietnam. The project is funded by The Fred Hollows Foundation. "We are excited to implement the EyeArt AI system to expand our capabilities in detection and treatment of diabetic retinopathy. It will help us reach our goal to protect the vision of approximately six million people with diabetes living in Vietnam," said Pham Quoc Anh, Vietnam Country Manager for The Fred Hollows Foundation. "This important project will help us continue the work first started by Professor Fred Hollows 29 years ago, fulfilling his vision to bring equitable eye health for all."
Glaucoma is a leading cause of irreversible blindness worldwide. A recent global meta-analysis of 50 population-based studies reported the pooled glaucoma prevalence (age range, 40-80 years) to be 3.5%, corresponding to an estimated 64.3 million individuals worldwide. Li, Zhixi et al. used deep learning system to detect referable GON (glaucomatous optic neuropathy) with high sensitivity and specificity. The study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON.
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. Deep learning 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.
"We would never have had the capability to understand the COVID-19 genome without the data science. It gave us, frankly, the computer power to understand what that looks like," Gorsky said in an interview with Jim Mazzo, president and CEO of Avellino. Powerful AI gave Johnson & Johnson better capabilities to develop a COVID-19 vaccine and then plan clinical trials with the sites that would offer beneficial insights into safety and efficacy, Gorsky said. When asked about the ongoing role of advancing technology, including AI, in health care and ophthalmology, Gorsky predicted a fundamentally changing landscape moving forward. "I think the health care landscape is going to be fundamentally changed in so many ways by the addition not only of artificial intelligence but of data science and connectivity of so many of these emerging fields becoming more and more ubiquitous across everything that we do," Gorsky said.
In a country such as India that has a low doctor-patient ratio, Artificial Intelligence (AI) can enable greater access to expert care from anywhere, with telehealth and robotics applied across inpatient and outpatient environments. Experts says AI will bolster the role of healthcare by assisting in screening, diagnosis, and treatment of diseases thereby improving quality of life and reducing the cost burden for patients. "India has a tremendous opportunity to lead human-centric applications and democratise AI for the world backed by high skilled talent, technology, vast data availability, and the potential for population-scale AI adoption," says Vice-president and managing director of Sales, Marketing and Communications Group, Intel India. Intel has been focusing its efforts towards accelerating AI innovation to deliver transformative healthcare solutions and democratise healthcare access and delivery in India. The company's portfolio of compute, memory, storage, and networking technologies powers some of the most exciting healthcare and life sciences applications.