Validating the Usefulness of the “Random Forests” Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography

Asaoka, Ryo; Hirasawa, Kazunori; Iwase, Aiko; Fujino, Yuri; Murata, Hiroshi; Shoji, Nobuyuki; Araie, Makoto

doi:10.1016/j.ajo.2016.11.001

Cited by 50 publications

(26 citation statements)

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“…As a result, the AROC to discriminate glaucomatous (from early to advanced glaucoma cases) from normative eyes was 98.5% 31 . We also reported that the AROC of this approach was 93.0% when discriminating early stage glaucoma patients and normative eyes 32 . Following great successes of deep learning methods for discrimination tasks in various fields, the application of these methods have just begun in the field of glaucoma.…”

Section: Discussionmentioning

confidence: 60%

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Shibata¹,

Tanito

Mitsuhashi³

et al. 2018

Sci Rep

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205

147

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The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval [CI]: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.

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Section: Discussionmentioning

confidence: 60%

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Shibata¹,

Tanito

Mitsuhashi³

et al. 2018

Sci Rep

Self Cite

205

147

View full text Add to dashboard Cite

show abstract

“…For the kernel-based model, a support vector machine (SVM) with a Gaussian kernel (RBF) has been widely adopted in many clinical applications, such as coronary artery disease prediction [25, 26]. For the decision tree approach, the random forest (RF) model [27–29] and the XGBoost model [30–32] have also been used in clinical research. Finally, a basic prediction technique [33], k-nearest neighbor algorithm (k-NN) was built [34].…”

Section: Methodsmentioning

confidence: 99%

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

et al. 2019

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Background Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. Objective To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. Methods The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. Results The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Conclusions Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice. Electronic supplementary material The online version of this article (10.1186/s12967-019-1860-0) contains supplementary material, which is available to authorized users.

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“…Recently, machine learning technologies and deep learning, in particular, have seen dramatic progress and has enabled the development of new algorithms to automate eye disease diagnosis [7, 8], including glaucoma screening based on color fundus images [9, 10] and OCT data [11, 12]. However, the proposed machine learning models in these studies dealt only with either kind of images to distinguish glaucoma patients from healthy subjects, which is quite different from the actual clinical diagnosis by ophthalmologists.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images

Omodaka

Hashimoto

et al. 2019

Journal of Healthcare Engineering

159

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This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

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Validating the Usefulness of the “Random Forests” Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography

Abstract: It is useful to analyze multiple SDOCT parameters concurrently using the Random Forests method to diagnose glaucoma in early stages.

Cited by 50 publications

References 50 publications

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images

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