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Gaire BP, Koronyo Y, Fuchs DT, Shi H, Rentsendorj A, Danziger R, Vit JP, Mirzaei N, Doustar J, Sheyn J, Hampel H, Vergallo A, Davis MR, Jallow O, Baldacci F, Verdooner SR, Barron E, Mirzaei M, Gupta VK, Graham SL, Tayebi M, Carare RO, Sadun AA, Miller CA, Dumitrascu OM, Lahiri S, Gao L, Black KL, Koronyo-Hamaoui M. Alzheimer's disease pathophysiology in the Retina. Prog Retin Eye Res 2024; 101:101273. [PMID: 38759947 DOI: 10.1016/j.preteyeres.2024.101273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.
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Affiliation(s)
- Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dieu-Trang Fuchs
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Haoshen Shi
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Altan Rentsendorj
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ron Danziger
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jean-Philippe Vit
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nazanin Mirzaei
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonah Doustar
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Julia Sheyn
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Miyah R Davis
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ousman Jallow
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Filippo Baldacci
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | | | - Ernesto Barron
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Mehdi Mirzaei
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Vivek K Gupta
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Stuart L Graham
- Department of Clinical Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia; Department of Clinical Medicine, Macquarie University, Sydney, NSW, Australia
| | - Mourad Tayebi
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
| | - Roxana O Carare
- Department of Clinical Neuroanatomy, University of Southampton, Southampton, UK
| | - Alfredo A Sadun
- Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA; Doheny Eye Institute, Los Angeles, CA, USA
| | - Carol A Miller
- Department of Pathology Program in Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Shouri Lahiri
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liang Gao
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Keith L Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Liu X, Wu J, Shao A, Shen W, Ye P, Wang Y, Ye J, Jin K, Yang J. Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study. J Med Internet Res 2024; 26:e51926. [PMID: 38252483 PMCID: PMC10845019 DOI: 10.2196/51926] [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] [Received: 08/17/2023] [Revised: 10/07/2023] [Accepted: 11/30/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical settings, as well as its reasoning, have not been explored in depth. OBJECTIVE This study aimed to evaluate ChatGPT's diagnostic performance and inference abilities for retinal vascular diseases in a non-English clinical environment. METHODS In this cross-sectional study, we collected 1226 fundus fluorescein angiography reports and corresponding diagnoses written in Chinese and tested ChatGPT with 4 prompting strategies (direct diagnosis or diagnosis with a step-by-step reasoning process and in Chinese or English). RESULTS Compared with ChatGPT using Chinese prompts for direct diagnosis that achieved an F1-score of 70.47%, ChatGPT using English prompts for direct diagnosis achieved the best diagnostic performance (80.05%), which was inferior to ophthalmologists (89.35%) but close to ophthalmologist interns (82.69%). As for its inference abilities, although ChatGPT can derive a reasoning process with a low error rate (0.4 per report) for both Chinese and English prompts, ophthalmologists identified that the latter brought more reasoning steps with less incompleteness (44.31%), misinformation (1.96%), and hallucinations (0.59%) (all P<.001). Also, analysis of the robustness of ChatGPT with different language prompts indicated significant differences in the recall (P=.03) and F1-score (P=.04) between Chinese and English prompts. In short, when prompted in English, ChatGPT exhibited enhanced diagnostic and inference capabilities for retinal vascular disease classification based on Chinese fundus fluorescein angiography reports. CONCLUSIONS ChatGPT can serve as a helpful medical assistant to provide diagnosis in non-English clinical environments, but there are still performance gaps, language disparities, and errors compared to professionals, which demonstrate the potential limitations and the need to continually explore more robust large language models in ophthalmology practice.
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Affiliation(s)
- Xiaocong Liu
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
| | - Jiageng Wu
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
| | - An Shao
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Wenyue Shen
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Panpan Ye
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Yao Wang
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Jie Yang
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
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Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
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Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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4
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Zhao PY, Bommakanti N, Yu G, Aaberg MT, Patel TP, Paulus YM. Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy. Sci Rep 2023; 13:9165. [PMID: 37280345 DOI: 10.1038/s41598-023-36327-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/01/2023] [Indexed: 06/08/2023] Open
Abstract
Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progression to the proliferative stage of diabetic retinopathy, which is an important distinction that requires timely ophthalmic intervention with laser or intravitreal injection treatment to reduce the risk of severe, permanent vision loss. In this study, we developed a deep learning algorithm to detect neovascular leakage on ultra-widefield fluorescein angiography images obtained from patients with diabetic retinopathy. The algorithm, an ensemble of three convolutional neural networks, was able to accurately classify neovascular leakage and distinguish this disease marker from other angiographic disease features. With additional real-world validation and testing, our algorithm could facilitate identification of neovascular leakage in the clinical setting, allowing timely intervention to reduce the burden of blinding diabetic eye disease.
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Affiliation(s)
- Peter Y Zhao
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Nikhil Bommakanti
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Gina Yu
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Michael T Aaberg
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Tapan P Patel
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Yannis M Paulus
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
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Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Front Cell Dev Biol 2023; 11:1168327. [PMID: 37056999 PMCID: PMC10086262 DOI: 10.3389/fcell.2023.1168327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunfang Liu
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
| | - Ying Zhao
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| | - Liya Zhang
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
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