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Zago Ribeiro L, Nakayama LF, Malerbi FK, Regatieri CVS. Automated machine learning model for fundus image classification by health-care professionals with no coding experience. Sci Rep 2024; 14:10395. [PMID: 38710726 PMCID: PMC11074250 DOI: 10.1038/s41598-024-60807-y] [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: 10/04/2023] [Accepted: 04/26/2024] [Indexed: 05/08/2024] Open
Abstract
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.
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Affiliation(s)
- Lucas Zago Ribeiro
- Department of Ophthalmology and Visual Sciences, Federal University of São Paulo, São Paulo, SP, Brazil.
| | - Luis Filipe Nakayama
- Department of Ophthalmology and Visual Sciences, Federal University of São Paulo, São Paulo, SP, Brazil
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, MA, USA
| | - Fernando Korn Malerbi
- Department of Ophthalmology and Visual Sciences, Federal University of São Paulo, São Paulo, SP, Brazil
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2
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Crincoli E, Sacconi R, Querques L, Querques G. Artificial intelligence in age-related macular degeneration: state of the art and recent updates. BMC Ophthalmol 2024; 24:121. [PMID: 38491380 PMCID: PMC10943791 DOI: 10.1186/s12886-024-03381-1] [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: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical management of AMD and support research for a better understanding of the disease. The aim of this review is to provide a panoramic description of all the applications of AI to AMD management and screening that have been analyzed in recent past literature. Deep learning (DL) can be effectively used to diagnose AMD, to predict short term risk of exudation and need for injections within the next 2 years. Moreover, DL technology has the potential to customize anti-VEGF treatment choice with a higher accuracy than expert human experts. In addition, accurate prediction of VA response to treatment can be provided to the patients with the use of ML models, which could considerably increase patients' compliance to treatment in favorable cases. Lastly, AI, especially in the form of DL, can effectively predict conversion to GA in 12 months and also suggest new biomarkers of conversion with an innovative reverse engineering approach.
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Affiliation(s)
- Emanuele Crincoli
- Ophthalmology Unit, "Fondazione Policlinico Universitario A. Gemelli IRCCS", Rome, Italy
| | - Riccardo Sacconi
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Lea Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Giuseppe Querques
- Department of Ophthalmology, University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
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3
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Heinke A, Zhang H, Deussen D, Galang CMB, Warter A, Kalaw FGP, Bartsch DUG, Cheng L, An C, Nguyen T, Freeman WR. ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION. Retina 2024; 44:465-474. [PMID: 37988102 PMCID: PMC10922109 DOI: 10.1097/iae.0000000000003977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
PURPOSE The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. METHODS Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. RESULTS The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). CONCLUSION These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.
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Affiliation(s)
- Anna Heinke
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Haochen Zhang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and
| | - Daniel Deussen
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
- University Eye Hospital, Ludwig-Maximillians-University, Munich, Germany
| | - Carlo Miguel B Galang
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Alexandra Warter
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Fritz Gerald P Kalaw
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Dirk-Uwe G Bartsch
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Lingyun Cheng
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
| | - Cheolhong An
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and
| | - Truong Nguyen
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and
| | - William R Freeman
- Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California
- Joan and Irwin Jacobs Retina Center, La Jolla, California
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and
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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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Lad EM, Finger RP, Guymer R. Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration. Ophthalmol Ther 2023; 12:2917-2941. [PMID: 37773477 PMCID: PMC10640447 DOI: 10.1007/s40123-023-00807-9] [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: 07/14/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.
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Affiliation(s)
- Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA.
| | - Robert P Finger
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robyn Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
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6
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Lin YT, Zhou Q, Tan J, Tao Y. Multimodal and multi-omics-based deep learning model for screening of optic neuropathy. Heliyon 2023; 9:e22244. [PMID: 38046141 PMCID: PMC10686864 DOI: 10.1016/j.heliyon.2023.e22244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 12/05/2023] Open
Abstract
Purpose To examine the use of multimodal data and multi-omics strategies for optic nerve disease screening. Methods This was a single-center retrospective study. A deep learning model was created from fundus photography and infrared reflectance (IR) images of patients with diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis. Patients who were seen at the Ophthalmology Department of First Affiliated Hospital of Nanchang University in Jiangxi Province from November 2019 to April 2023 were included in this study. The data were analyzed in single and multimodal modes following the traditional omics, Resnet101, and fusion models. The accuracy and area-under-the-curve (AUC) of each model were compared. Results A total of 312 images fundus and infrared fundus photographs were collected from 156 patients. When multi-modal data was used, the accuracy of the traditional omics mode, Resnet101, and fusion models with the training set were 0.97, 0.98, and 0.99, respectively. The accuracy of the same models with the test sets were 0.72, 0.87, and 0.88, respectively. We compared single- and multi-mode states by applying the data to the different groups in the learning model. In the traditional omics model, the macro-average AUCs of the features extracted from fundus photography, IR images, and multimodal data were 0.94, 0.90, and 0.96, respectively. When the same data were processed in the Resnet101 model, the scores were 0.97 equally. However, when multimodal data was utilized, the macro-average AUCs in the traditional omics, Resnet101, and fusion modesl were 0.96, 0.97, and 0.99, respectively. Conclusion The deep learning model based on multimodal data and multi-omics strategies can improve the accuracy of screening and diagnosing diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis.
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Affiliation(s)
- Ye-ting Lin
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Qiong Zhou
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Jian Tan
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Yulin Tao
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
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7
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Koseoglu ND, Grzybowski A, Liu TYA. Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review. Ophthalmol Ther 2023; 12:2347-2359. [PMID: 37493854 PMCID: PMC10441995 DOI: 10.1007/s40123-023-00775-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.
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Affiliation(s)
- Neslihan Dilruba Koseoglu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - T Y Alvin Liu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA.
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8
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Leandro I, Lorenzo B, Aleksandar M, Dario M, Rosa G, Agostino A, Daniele T. OCT-based deep-learning models for the identification of retinal key signs. Sci Rep 2023; 13:14628. [PMID: 37670066 PMCID: PMC10480174 DOI: 10.1038/s41598-023-41362-4] [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: 05/15/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023] Open
Abstract
A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models' potential to improve ocular pathology diagnosis and clinical decision-making.
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Affiliation(s)
- Inferrera Leandro
- Department of Medicine, Surgery and Health Sciences, Eye Clinic, Ophthalmology Clinic, University of Trieste, Piazza Dell'Ospitale 1, 34125, Trieste, Italy.
| | - Borsatti Lorenzo
- Department of Medicine, Surgery and Health Sciences, Eye Clinic, Ophthalmology Clinic, University of Trieste, Piazza Dell'Ospitale 1, 34125, Trieste, Italy
| | | | - Marangoni Dario
- Department of Medicine, Surgery and Health Sciences, Eye Clinic, Ophthalmology Clinic, University of Trieste, Piazza Dell'Ospitale 1, 34125, Trieste, Italy
| | - Giglio Rosa
- Department of Medicine, Surgery and Health Sciences, Eye Clinic, Ophthalmology Clinic, University of Trieste, Piazza Dell'Ospitale 1, 34125, Trieste, Italy
| | - Accardo Agostino
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Tognetto Daniele
- Department of Medicine, Surgery and Health Sciences, Eye Clinic, Ophthalmology Clinic, University of Trieste, Piazza Dell'Ospitale 1, 34125, Trieste, Italy
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9
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Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers. PLOS DIGITAL HEALTH 2023; 2:e0000106. [PMID: 36812608 PMCID: PMC9931262 DOI: 10.1371/journal.pdig.0000106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/14/2023] [Indexed: 02/17/2023]
Abstract
Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.
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10
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Sheng B, Chen X, Li T, Ma T, Yang Y, Bi L, Zhang X. An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front Public Health 2022; 10:971943. [PMID: 36388304 PMCID: PMC9650481 DOI: 10.3389/fpubh.2022.971943] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/04/2022] [Indexed: 01/25/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.
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Affiliation(s)
- Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Xiaosi Chen
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tingyao Li
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Tianxing Ma
- Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing, China
| | - Yang Yang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Bi
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Xinyuan Zhang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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11
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Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S50-S57. [PMID: 36216736 PMCID: PMC9732476 DOI: 10.1016/j.optom.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.
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Affiliation(s)
- Lorenzo Ferro Desideri
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
| | | | - Paolo Corazza
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | | | - Matilde Roda
- Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy
| | - Massimo Nicolo
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Carlo Enrico Traverso
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Aldo Vagge
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
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