Machine Learning Techniques in Clinical Vision Sciences

Caixinha, Miguel; Nunes, Sandrina

doi:10.1080/02713683.2016.1175019

Cited by 67 publications

(53 citation statements)

References 84 publications

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“…Less variation in experimental design is also reported in studies using the rabbit model compared to the mouse model. The McKrae strain is typically used to infect the non-scarified eye at a concentration of 2 × 10 5 PFU/eye, due to this strain’s high reactivation frequency [ 144 , 145 , 146 ]. However, other strains of HSV-1 (Rodanus, RE, F, KOS, 17Syn+ and E-43) can also spontaneous reactivate and are, therefore, occasionally used [ 57 , 147 ].…”

Section: In Vivo Systemsmentioning

confidence: 99%

New Paradigms for the Study of Ocular Alphaherpesvirus Infections: Insights into the Use of Non-Traditional Host Model Systems

Pennington

Ledbetter

Walle

2017

Viruses

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Ocular herpesviruses, most notably human alphaherpesvirus 1 (HSV-1), canid alphaherpesvirus 1 (CHV-1) and felid alphaherpesvirus 1 (FHV-1), infect and cause severe disease that may lead to blindness. CHV-1 and FHV-1 have a pathogenesis and induce clinical disease in their hosts that is similar to HSV-1 ocular infections in humans, suggesting that infection of dogs and cats with CHV-1 and FHV-1, respectively, can be used as a comparative natural host model of herpesvirus-induced ocular disease. In this review, we discuss both strengths and limitations of the various available model systems to study ocular herpesvirus infection, with a focus on the use of these non-traditional virus-natural host models. Recent work has demonstrated the robustness and reproducibility of experimental ocular herpesvirus infections in dogs and cats, and, therefore, these non-traditional models can provide additional insights into the pathogenesis of ocular herpesvirus infections.

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Section: In Vivo Systemsmentioning

confidence: 99%

New Paradigms for the Study of Ocular Alphaherpesvirus Infections: Insights into the Use of Non-Traditional Host Model Systems

Pennington

Ledbetter

Walle

2017

Viruses

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“…Many previous studies have focused on automated detection of retinal diseases by using machine learning algorithms in order to analyze a large number of fundus photographs taken from retinal screening programs [ 6 , 7 ]. Various machine learning algorithms—K-nearest neighbor algorithm, Naive Bayes classifier, artificial neural network (ANN), and support vector machine (SVM)—were applied to automated retinal disease detection [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

et al. 2017

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Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

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“…Machine learning techniques are used to automatically recognize complex patterns in a given dataset (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), where a group label, such as a disease, is available for each case. 12 To ensure good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. Miguel Caixinha and Sandrina Nunes introduced conventional machine learning (CML) techniques and reviewed applications of CML for diagnosis and monitoring of multimodal ocular disease.…”

Section: Introductionmentioning

confidence: 99%

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans

Russakoff¹,

Mannil

Oakley³

et al. 2020

Trans. Vis. Sci. Tech.

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Citation: Russakoff DB, Mannil SS, Oakley JD, Ran AR, Cheung CY, Dasari S, Riyazzuddin M, Nagaraj S, Rao HL, Chang D, Chang RT. A 3D deep learning system for detecting referable glaucoma using full OCT macular cube scans. Trans Vis Sci Tech. 2020;9(2):12, https://doi.org/10. 1167/tvst.9.2.12 Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance.Methods: There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively. Results:The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively. Conclusions:A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia.Translational Relevance: Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.

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Machine Learning Techniques in Clinical Vision Sciences

Cited by 67 publications

References 84 publications

New Paradigms for the Study of Ocular Alphaherpesvirus Infections: Insights into the Use of Non-Traditional Host Model Systems

New Paradigms for the Study of Ocular Alphaherpesvirus Infections: Insights into the Use of Non-Traditional Host Model Systems

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans

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