Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Abstract

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device

“…and (b) different methodologies for data processing and analysis (e.g., the usage of supervised or unsupervised the choice of machine or deep learning approaches, the type of classifiers, the number of extracted features, etc. ), our results align with other reported results based on supervised ML approaches in the existing literature [53,54], as shown in Table 3. Another important finding of our study was the identical classification accuracy in terms of eye selection (approximately 97%) with the RETeval device, while there was a discrepancy in the OCT data (85.5% for the right eye and 75% for the left, respectively).…”

Transforming Glaucoma Care with Artificial Intelligence: A Comprehensive Review of Diagnosis, Monitoring, and Treatment Algorithms

“…The interpretation of these results guides clinicians in initiating treatment or determining the need for ongoing evaluations to arrive at a definitive diagnosis. Given the image-dependent nature of Ophthalmology, there is extensive potential for leveraging artificial intelligence (AI) to improve the diagnosis, treatment effectiveness, and other aspects of managing ocular diseases, particularly in the detection, diagnosis, treatment, and care of glaucoma [5]. Implementing predictive algorithms to forecast outcomes using varied treatment approaches can significantly improve treatment recommendations and patient compliance [6].…”