Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.

Computational method for aid in the diagnosis of sixth optic nerve palsy through digital videos

The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela. The usual methods for diagnosing the sixth nerve palsy are invasive or depend on expensive equipment, and computer-based methods designed specifically to diagnose the aforementioned palsy were not found until the publication of this work. Therefore, a low-cost, non-invasive method can support or guide the ophthalmologist's diagnosis. In this context, this work presents a computational methodology to aid in diagnosing the sixth nerve palsy using videos to assist ophthalmologists in the diagnostic process, serving as a second opinion. The proposed method uses convolutional neural networks and image processing techniques to track both eyes' movement trajectory during the video. With this trajectory, it is possible to calculate the average velocity (AV) in which each eye moves. Since it is known that paretic eyes move slower than healthy eyes, comparing the AV of both eyes can determine if the eye is healthy or paretic. The results obtained with the proposed method showed that paretic eyes move at least 19.65% slower than healthy ones. This threshold, along with the AV of the movement of the eyes, can help ophthalmologists in their analysis. The proposed method reached 92.64% accuracy in diagnosing the sixth optic nerve palsy (SONP), with a Kappa index of 0.925, which highlights the reliability of the results and gives favorable perspectives for further clinical application.

Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term 'AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.