Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images

Mehta, Parmita; Petersen, Christine A.; Wen, Joanne C; Banitt, Michael R.; Chen, Philip; Bojikian, Karine D.; Egan, Catherine; Lee, Su-In; Balazinska, Magdalena; Lee, Aaron; Rokem, Ariel

doi:10.1016/j.ajo.2021.04.021

Cited by 63 publications

(24 citation statements)

References 94 publications

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“…The standard modality of assessing structure, though, is OCT imaging; algorithms can provide assessment of the anterior chamber angle as well as segmentation of the RNFL adjusted for other parameters (age, gender, and eye biometry metrics) to improve the accuracy of the measurements (344)(345)(346). Studies have focused on many parameters of the retina and ONH (RNFL, prelaminar area, RPE, choroid, peripapillary sclera, Bruch membrane opening, and minimum rim width), and their performance was highly accurate in identifying glaucomatous eyes (>94%); AI analysis of OCT-A vascular abnormalities of the ONH also yields excellent results (347)(348)(349)(350)(351)(352)(353)(354). When comparing various ML classifiers, Wu et al showed that ganglion cell layer measurements were important in early glaucoma detection, whereas RNFL metrics were more useful during disease progression; in fact, Shin et al showed that wide-field SS-OCT scans can even outperform the conventional parameter-based methods (355)(356)(357).…”

Section: Artificial Intelligence and Glaucomamentioning

confidence: 99%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

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Section: Artificial Intelligence and Glaucomamentioning

confidence: 99%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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“…AI has been used in ophthalmology to diagnose diseases in conjunction with imaging technologies such as optical coherence tomography, and fundus fluorescein angiography ( Ruiz Hidalgo et al, 2017 ; Hemalakshmi et al, 2020 ; Wan et al, 2021c ; Ran et al, 2021 ). In addition, several simple and low-cost diagnostic system models have been under development ( Bourouis et al, 2014 ; Metha et al, 2021 ). As a possible solution for the screening of major ophthalmic diseases and telemedicine, AI has been applied to the study of ophthalmic disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Zhao

Zhu

et al. 2022

Front. Pharmacol.

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Background: Artificial intelligence (AI) has been used in the research of ophthalmic disease diagnosis, and it may have an impact on medical and ophthalmic practice in the future. This study explores the general application and research frontier of artificial intelligence in ophthalmic disease detection.Methods: Citation data were downloaded from the Web of Science Core Collection database to evaluate the extent of the application of Artificial intelligence in ophthalmic disease diagnosis in publications from 1 January 2012, to 31 December 2021. This information was analyzed using CiteSpace.5.8. R3 and Vosviewer.Results: A total of 1,498 publications from 95 areas were examined, of which the United States was determined to be the most influential country in this research field. The largest cluster labeled “Brownian motion” was used prior to the application of AI for ophthalmic diagnosis from 2007 to 2017, and was an active topic during this period. The burst keywords in the period from 2020 to 2021 were system, disease, and model.Conclusion: The focus of artificial intelligence research in ophthalmic disease diagnosis has transitioned from the development of AI algorithms and the analysis of abnormal eye physiological structure to the investigation of more mature ophthalmic disease diagnosis systems. However, there is a need for further studies in ophthalmology and computer engineering.

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“…However, what most people criticize MLCs for is the analysis process being a "black box" [25][26][27], for it produces results based solely on the input data using an algorithm, which prevents clinicians from understanding how variables are being combined to make such a prediction. Through program analysis methods developed in recent years, we can study and analyze the importance of the included parameters in specific models, and ophthalmologists may obtain clinical insight from the explanation [24,25,[28][29][30]. In this study, we aim to build MLCs based on clinical OCT data provided by Heidelberg Spectralis spectral-domain OCT (SD-OCT) to evaluate the diagnostic accuracy of MLCs and the importance of OCT parameters in diagnosing glaucoma of varying severities.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

Shen

et al. 2021

Diagnostics

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Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

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Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images

Cited by 63 publications

References 94 publications

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

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