Clinical Applications of Artificial Intelligence in Glaucoma

Yousefi, Siamak

doi:10.18502/jovr.v18i1.12730

Cited by 14 publications

(14 citation statements)

References 102 publications

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“…One of the differential facts of our work is that we analyzed the results by clinical stages of glaucoma, from initial to advanced cases. Few studies have addressed the performance of AI techniques based on the evolutionary stages of glaucoma [32].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Cellini,

Caamaño,

Carrasco

et al. 2023

Ophthalmic Res

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Introduction: Artificial intelligence has real potential for early identification of ocular diseases such as glaucoma. An important challenge is the requirement for large databases properly selected, which are not easily obtained. We used a relatively original strategy: a glaucoma recognition algorithm trained with fundus images from public databases and then tested and retrained with a carefully selected patient database. Methods: The study’s supervised deep learning method was an adapted version of the ResNet-50 architecture previously trained from 10,658 optic head images (glaucomatous or non-glaucomatous) from seven public databases. A total of 1,158 new images labeled by experts from 616 patients were added. The images were categorized after clinical examination including visual fields in 304 (26%) control images or those with ocular hypertension and 347 (30%) images with early, 290 (25%) with moderate, and 217 (19%) with advanced glaucoma. The initial algorithm was tested using 30% of the selected glaucoma database and then re-trained with 70% of this database and tested again. Results: The results in the initial sample showed an area under the curve (AUC) of 76% for all images, and 66% for early, 82% for moderate, and 84% for advanced glaucoma. After retraining the algorithm, the respective AUC results were 82%, 72%, 89%, and 91%. Conclusion: Using combined data from public databases and data selected and labeled by experts facilitated improvement of the system’s precision and identified interesting possibilities for obtaining tools for automatic screening of glaucomatous eyes more affordably.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Cellini,

Caamaño,

Carrasco

et al. 2023

Ophthalmic Res

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“…Parallel to the rise of AI in medicine has been the development of robotics for healthcare applications [6]. Humanoid robots, easily recognizable by their human-like characteristics, are receiving more attention in healthcare research [7][8][9][10][11]. When paired with AI, humanoid robots can provide a more natural patient interface, thus may benefit both clinical operations and patient engagement.…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends

Madadi,

Delsoz,

Khouri

et al. 2024

Current Opinion in Ophthalmology

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Purpose of review Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology. Recent findings Some recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration. Summary Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.

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“…Previous studies have shown that deep learning models individually trained on color fundus photos [10], visual field analysis [11][12][13][14], and optical coherence tomography (OCT) [15][16][17][18][19] are able to identify glaucomatous optic neuropathy (GON) with robust performance (comparisons of specific deep learning models developed for glaucoma diagnosis and discussions of the different approaches are thoroughly covered in excellent reviews from Thompson et al [5] and Yousefi [20]). Indeed, a recent meta-analysis of 17 deeplearning models trained on diagnosing GON from fundus photographs reported an overall AUC of 0.93 (95% CI 0.92-0.94), slightly lower than the AUC reported for studies using OCT (overall AUC 0.96, 95% CI 0.94-0.98) [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs

Hwang,

Chen,

Han

et al. 2023

Bioengineering

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Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus photography, a widely available and low-cost approach already adopted for automated screening of ophthalmic diseases such as diabetic retinopathy. Despite this, the lack of validated early screening approaches remains a major obstacle in the prevention of glaucoma-related blindness. Deep learning models have gained significant interest as potential solutions, as these models offer objective and high-throughput methods for processing image-based medical data. While convolutional neural networks (CNN) have been widely utilized for these purposes, more recent advances in the application of Transformer architectures have led to new models, including Vision Transformer (ViT,) that have shown promise in many domains of image analysis. However, previous comparisons of these two architectures have not sufficiently compared models side-by-side with more than a single dataset, making it unclear which model is more generalizable or performs better in different clinical contexts. Our purpose is to investigate comparable ViT and CNN models tasked with GON detection from fundus photos and highlight their respective strengths and weaknesses. We train CNN and ViT models on six unrelated, publicly available databases and compare their performance using well-established statistics including AUC, sensitivity, and specificity. Our results indicate that ViT models often show superior performance when compared with a similarly trained CNN model, particularly when non-glaucomatous images are over-represented in a given dataset. We discuss the clinical implications of these findings and suggest that ViT can further the development of accurate and scalable GON detection for this leading cause of irreversible blindness worldwide.

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Clinical Applications of Artificial Intelligence in Glaucoma

Cited by 14 publications

References 102 publications

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs

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