The use of deep learning technology for the detection of optic neuropathy

Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into medicine, including ophthalmology, owing to its strong capabilities in image recognition. Methods: This review focuses on the most recent key applications of AI in the diagnosis and management of, as well as research on, glaucoma by performing a systematic review of the latest papers in the literature. Results: In glaucoma, AI can help analyze large amounts of data from diagnostic tools, such as fundus images, optical coherence tomography scans, and visual field tests. Conclusions: AI technologies can enhance the accuracy of glaucoma diagnoses and could provide significant economic benefits by automating routine tasks, improving diagnostic accuracy, and enhancing access to care, especially in underserved areas. However, despite these promising results, challenges persist, including limited dataset size and diversity, class imbalance, the need to optimize models for early detection, and the integration of multimodal data into clinical practice. Currently, ophthalmologists are expected to continue playing a leading role in managing glaucomatous eyes and overseeing the development and validation of AI tools.

Bibliometric analysis of research on the application of deep learning to ophthalmology

Background

Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.

Methods

This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024. Visualization of similarities (VOS)-viewer was used to analyze and evaluate 3,055 articles. Data from the articles were collected on a specific date (September 11, 2024) and downloaded from the Web of Science Core Collection (WOSCC) in plain-text format.

Results

A total of 3,055 relevant articles on the WOSCC published from 2015 to 2024 were included in this analysis. The first article on the application of deep learning to ophthalmology was published in 2015, and the number of articles on the subject has grown significantly since 2019. China was the most productive country (n=1,187), followed by the United States (n=673). Sun Yat-sen University was the institution with the most publications. Cheng and Bogunovic were the most frequently published authors. The following four different clusters were identified based on a co-occurrence cluster analysis of high-frequency keywords: (I) deep learning for the segmentation and feature extraction of ophthalmic images; (II) deep learning for the automatic detection and classification of ophthalmic images; (III) application of deep learning to ophthalmic imaging techniques; and (IV) deep learning for the diagnosis and management of ophthalmic diseases.

Conclusions

The analysis of fundus images and the clinical application of deep learning techniques have emerged as prominent research areas in the field of ophthalmology. The substantial increase in publications and citations signifies the expanding impact and global collaboration in the application of deep learning research to ophthalmology. By identifying four distinct clusters representing sub-topics in deep learning ophthalmology research, this study contributes to the understanding of current trends and potential future advancements in the field.

Recent advances in the use of optical coherence tomography in neuro-ophthalmology: A review

Optical coherence tomography (OCT) is an in vivo imaging modality that provides non-invasive, high resolution and fast cross-sectional images of the optic nerve head, retina and choroid. OCT angiography (OCTA) is an emerging tool. It is a non-invasive, dye-free imaging approach of visualising the microvasculature of the retina and choroid by employing motion contrast imaging for blood flow detection and is gradually receiving attention for its potential roles in various neuro-ophthalmic and retinal conditions. We will review the clinical utility of the OCT in the management of various common neuro-ophthalmic and neurological disorders. We also review some of the OCTA research findings in these conditions. Finally, we will discuss the limitations of OCT as well as introduce other emerging technologies.