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Kang D, Wu H, Yuan L, Shi Y, Jin K, Grzybowski A. A Beginner's Guide to Artificial Intelligence for Ophthalmologists. Ophthalmol Ther 2024; 13:1841-1855. [PMID: 38734807 PMCID: PMC11178755 DOI: 10.1007/s40123-024-00958-3] [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: 03/19/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.
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Affiliation(s)
- Daohuan Kang
- Department of Ophthalmology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Hongkang Wu
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lu Yuan
- Department of Ophthalmology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yu Shi
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Zhejiang University School of Medicine, Hangzhou, China
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
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Akpinar MH, Sengur A, Faust O, Tong L, Molinari F, Acharya UR. Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108253. [PMID: 38861878 DOI: 10.1016/j.cmpb.2024.108253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/25/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVES Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.
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Affiliation(s)
- Muhammed Halil Akpinar
- Department of Electronics and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Louis Tong
- Singapore Eye Research Institute, Singapore, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Sendecki A, Ledwoń D, Nycz J, Wąsowska A, Boguszewska-Chachulska A, Mitas AW, Wylęgała E, Teper S. A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study. Acta Ophthalmol 2024. [PMID: 38761033 DOI: 10.1111/aos.16710] [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: 01/11/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024]
Abstract
PURPOSE Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans. METHODS The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model. RESULTS The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R2 = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R2 = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis. CONCLUSION The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.
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Affiliation(s)
- Adam Sendecki
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Julia Nycz
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Anna Wąsowska
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
- Genomed S.A., Warszawa, Poland
| | | | - Andrzej W Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Sławomir Teper
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
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Tombolini B, Crincoli E, Sacconi R, Battista M, Fantaguzzi F, Servillo A, Bandello F, Querques G. Optical Coherence Tomography Angiography: A 2023 Focused Update on Age-Related Macular Degeneration. Ophthalmol Ther 2024; 13:449-467. [PMID: 38180632 PMCID: PMC10787708 DOI: 10.1007/s40123-023-00870-2] [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: 09/14/2023] [Accepted: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
Optical coherence tomography angiography (OCTA) has extensively enhanced our comprehension of eye microcirculation and of its associated diseases. In this narrative review, we explored the key concepts behind OCTA, as well as the most recent evidence in the pathophysiology of age-related macular degeneration (AMD) made possible by OCTA. These recommendations were updated since the publication in 2020, and are targeted for 2023. Importantly, as a future perspective in OCTA technology, we will discuss how artificial intelligence has been applied to OCTA, with a particular emphasis on its application to AMD study.
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Affiliation(s)
- Beatrice Tombolini
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Emanuele Crincoli
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Riccardo Sacconi
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Marco Battista
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Federico Fantaguzzi
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Andrea Servillo
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Francesco Bandello
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Giuseppe Querques
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
- Division of Head and Neck, Ophthalmology Unit, Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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