1
|
Lim G, Elangovan K, Jin L. Vision language models in ophthalmology. Curr Opin Ophthalmol 2024; 35:487-493. [PMID: 39259649 DOI: 10.1097/icu.0000000000001089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
PURPOSE OF REVIEW Vision Language Models are an emerging paradigm in artificial intelligence that offers the potential to natively analyze both image and textual data simultaneously, within a single model. The fusion of these two modalities is of particular relevance to ophthalmology, which has historically involved specialized imaging techniques such as angiography, optical coherence tomography, and fundus photography, while also interfacing with electronic health records that include free text descriptions. This review then surveys the fast-evolving field of Vision Language Models as they apply to current ophthalmologic research and practice. RECENT FINDINGS Although models incorporating both image and text data have a long provenance in ophthalmology, effective multimodal Vision Language Models are a recent development exploiting advances in technologies such as transformer and autoencoder models. SUMMARY Vision Language Models offer the potential to assist and streamline the existing clinical workflow in ophthalmology, whether previsit, during, or post-visit. There are, however, also important challenges to be overcome, particularly regarding patient privacy and explainability of model recommendations.
Collapse
|
2
|
Hallaj S, Chuter BG, Lieu AC, Singh P, Kalpathy-Cramer J, Xu BY, Christopher M, Zangwill LM, Weinreb RN, Baxter SL. Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives. Ophthalmol Glaucoma 2024:S2589-4196(24)00143-1. [PMID: 39214457 DOI: 10.1016/j.ogla.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
CLINICAL RELEVANCE Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of universal diagnostic tools. Developing robust artificial intelligence (AI) models for glaucoma screening is essential for early detection and treatment but faces significant obstacles. Effective deep learning algorithms require large, well-curated datasets from diverse patient populations and imaging protocols. However, creating centralized data repositories is hindered by concerns over data sharing, patient privacy, regulatory compliance, and intellectual property. Federated Learning (FL) offers a potential solution by enabling data to remain locally hosted while facilitating distributed model training across multiple sites. METHODS A comprehensive literature review was conducted on the application of Federated Learning in training AI models for glaucoma screening. Publications from 1950 to 2024 were searched using databases such as PubMed and IEEE Xplore with keywords including "glaucoma," "federated learning," "artificial intelligence," "deep learning," "machine learning," "distributed learning," "privacy-preserving," "data sharing," "medical imaging," and "ophthalmology." Articles were included if they discussed the use of FL in glaucoma-related AI tasks or addressed data sharing and privacy challenges in ophthalmic AI development. RESULTS FL enables collaborative model development without centralizing sensitive patient data, addressing privacy and regulatory concerns. Studies show that FL can improve model performance and generalizability by leveraging diverse datasets while maintaining data security. FL models have achieved comparable or superior accuracy to those trained on centralized data, demonstrating effectiveness in real-world clinical settings. CONCLUSIONS Federated Learning presents a promising strategy to overcome current obstacles in developing AI models for glaucoma screening. By balancing the need for extensive, diverse training data with the imperative to protect patient privacy and comply with regulations, FL facilitates collaborative model training without compromising data security. This approach offers a pathway toward more accurate and generalizable AI solutions for glaucoma detection and management. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.
Collapse
Affiliation(s)
- Shahin Hallaj
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Benton G Chuter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Alexander C Lieu
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Praveer Singh
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado
| | - Benjamin Y Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California.
| |
Collapse
|
3
|
Zhu Y, Salowe R, Chow C, Li S, Bastani O, O'Brien JM. Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection. Bioengineering (Basel) 2024; 11:122. [PMID: 38391608 PMCID: PMC10886285 DOI: 10.3390/bioengineering11020122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.
Collapse
Affiliation(s)
- Yan Zhu
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rebecca Salowe
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Caven Chow
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shuo Li
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Osbert Bastani
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joan M O'Brien
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
4
|
Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA. [Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma]. Vestn Oftalmol 2024; 140:80-85. [PMID: 39254394 DOI: 10.17116/oftalma202414004180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring and treatment, presents technologies that use machine learning, including neural networks, to predict disease progression and determine the need for anti-glaucoma surgery. The article also discusses the methods of personalized treatment based on projection machine learning methods and outlines the problems and prospects of using AI in solving tasks related to screening, diagnosing, and treating glaucoma.
Collapse
Affiliation(s)
- N I Kurysheva
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
| | - O Ye Rodionova
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - A L Pomerantsev
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - G A Sharova
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- OOO Glaznaya Klinika Doktora Belikovoy, Moscow, Russia
| |
Collapse
|