Researchers have devised an innovative machine learning model that uses retinal images to distinguish normal cognitive function from mild cognitive impairment. This model represents a new, non-invasive, and cost-effective method to detect early signs of cognitive decline, which could lead to Alzheimer’s disease.
Artificial intelligence (AI) and machine learning were once considered elements of science fiction, often portrayed as going rogue in movies like “Terminator,” “2001: A Space Odyssey,” and “The Matrix.” However, with the advent of the Fourth Industrial Revolution, AI has seamlessly integrated into our daily lives through cyberphysical systems—a fusion of revolutionary technologies such as AI, machine learning, robotics, 3D printing, autonomous vehicles, and more.
Today, AI is prevalent in various aspects of our lives. Smartphone digital assistants like Google Now, Siri, Alexa, Cortana, and Bixby employ AI, as does Gmail with its email filters, smart replies, and reminders. Other platforms such as Facebook use AI for tasks like newsfeed curation and image recognition, while Amazon provides personalized product recommendations, and Maps facilitates route planning with real-time traffic data. Chatbots are becoming increasingly common, and AI’s influence extends even further.
In the field of healthcare, AI is making significant strides, particularly in dermatology, radiology, pathology, and ophthalmology. Ophthalmology, being highly visual, relies on analyzing numerous images, ranging from fundus images to retinal SD-OCT and corneal topography. This visual nature offers exciting possibilities for AI and machine learning to aid in image processing and analysis.
Deep learning, a subset of machine learning, uses convolutional neural networks (CNNs) and has been employed in ophthalmology since 2016. Google’s algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs demonstrated high sensitivity and specificity. However, the Black Box problem posed a challenge, as the AI couldn’t explain the features used to arrive at a diagnosis. Later, Google’s Integrated Gradients Explanation helped address this issue and improved the accuracy and confidence of AI-assisted grading.
Major breakthroughs in AI have occurred in analyzing fundus images for conditions such as diabetic retinopathy, age-related macular degeneration, glaucoma, and more. Additionally, AI has been explored in grading cataracts, predicting myopia progression, and detecting ocular surface squamous neoplasia. These advancements have shown great potential.
Various studies have focused on AI screening of fundus photos, demonstrating high sensitivity and specificity in detecting diabetic retinopathy. In fact, IDx-DR became the first FDA-approved AI software for screening fundus photos for this condition. Smartphone-based fundus cameras, like DIYretCAM and T3Retcam, offer affordable alternatives for capturing fundus images.
The accompanying review article, “Artificial Intelligence in Diabetic Retinopathy: A Natural Step to the Future,” examines different AI and deep learning techniques used in screening fundus images. The diverse range of techniques signals a promising future for AI in healthcare. While concerns about AI replacing human ophthalmologists exist, experts assure us that AI will augment our clinical capabilities, rather than replace us.
In conclusion, the growing influence of AI in various fields, especially ophthalmology, promises exciting technological advancements in the future. As we embrace these innovations, we can remain optimistic about AI’s positive impact on healthcare and society as a whole.
Source National Library of Medecine