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REVIEW OF MACHINE LEARNING TECHNIQUES IN OPHTHALMOLOGY: A NOVEL APPROACH
2020
International Journal of Technical Research & Science
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In this paper, we concentrated on the survey of different existing machine learning models utilized for building up a determination framework for human services applications. ...
Advanced technology-based computer-aided diagnosis tools using machine learning techniques help to reduce the workload of the ophthalmologist. ...
At last, in this section, diseases diagnosed by Machine Learning Techniques is shown by block diagram represented by Figure2. ...
doi:10.30780/specialissue-icaccg2020/011
fatcat:4h2rcfgv2fhllkoryq2ca3y2rm
Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva
2016
Computational and Mathematical Methods in Medicine
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In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. ...
the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. ...
María Luisa Sánchez Brea acknowledges the support of the University of A Coruna though the Inditex-UDC Grant Program. ...
doi:10.1155/2016/3695014
pmid:28096890
pmcid:PMC5206783
fatcat:qsmzfryjxjbgbn3vxaqan4zhze
Evaluation of Convolutional Neural Network Model for Classifying Red and Healthy Eye
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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This paper presents a framework for classifying normal versus red eyes using deep learning technique of convolution neural network (CNN). The model has shown promising results with 94% accuracy. ...
Conjunctiva hyperemia refers to the redness of the conjunctiva. ...
In [7] large number of features were defined and it compared various machine learning techniques in grading the redness of eye namely classification and regression and transformed large number of features ...
doi:10.35940/ijitee.l2568.1081219
fatcat:7o6pscke6ndxpkp6jtwsrtglea
Classifying Red and Healthy Eyes using Deep Learning
2019
International Journal of Advanced Computer Science and Applications
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This paper highlights the work done so far for measuring the level of redness in the eye using various methodologies ranging from statistical ways to machine learning techniques and proposes a methodology ...
This condition is also termed as hyperemia. The study of this development is vital in diagnosis of various pathologies. ...
ACKNOWLEDGMENTS We would like to thank Sushant School of Health Science, Ansal University, for helping us in providing test images, clinical guidance and expertise. ...
doi:10.14569/ijacsa.2019.0100772
fatcat:5a5akxwu2bb3lgetwq6wmra6ky
Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization
2020
Annals of Translational Medicine
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Artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) techniques has gained tremendous global interest in this era. ...
In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and our prospects. ...
Deep learning (DL) is a class of state-of-the-art machine learning techniques that has sparked tremendous global interest in recent years (3) . ...
doi:10.21037/atm-20-976
pmid:32617334
pmcid:PMC7327317
fatcat:map3u5bhmramjj5qgfqkyfuate
A Fully Automated Pipeline for a Robust Conjunctival Hyperemia Estimation
2021
Applied Sciences
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Methods: In this work, we introduce a fully-automated analysis of the redness grading scales able to completely automatize the clinical procedure, starting from the acquired image to the redness estimation ...
Lastly, we implemented a predictive model for the conjunctival hyperemia using these features. ...
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper. ...
doi:10.3390/app11072978
fatcat:shfoi4irzzbwvl343whhox47bi
Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis
2018
European Radiology
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Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). ...
Objectives To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis ...
In contrast to classical machine learning-based approaches, the DL algorithm is able to independently learn generic and complex LVM patterns, and could potentially be more sensitive to changes in the LVM ...
doi:10.1007/s00330-018-5822-3
pmid:30421020
fatcat:mzgj3jattrdd5hjtioixazpuki
Front Matter: Volume 10341
2017
Ninth International Conference on Machine Vision (ICMV 2016)
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Additional papers and presentation recordings may be available online in the SPIE Digital Library at SPIEDigitalLibrary.org. ...
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. ...
[10341-29]
10341 1T
On the analysis of local and global features for hyperemia grading [10341-93]
10341 1U
Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring [10341-30] ...
doi:10.1117/12.2276832
dblp:conf/icmv/X16
fatcat:srr4hyfwpfcipadcvjc5jdll6i
Artificial Intelligence in Cardiovascular Atherosclerosis Imaging
2022
Journal of Personalized Medicine
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Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas ...
At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled ...
Sheet et al. collected 13 isolated hearts, using a machine learning framework to identify real necrotic areas of plaques in the IVUS, which is a marker of vulnerable plaques. ...
doi:10.3390/jpm12030420
pmid:35330420
pmcid:PMC8952318
fatcat:yp5oxbjajzfmriqxp7qotchc3y
Accuracy of Funduscopy to Identify True Edema versus Pseudoedema of the Optic Disc
2012
Investigative Ophthalmology and Visual Science
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Accuracy, sensitivity, and specificity from all possible combinations of signs were calculated by support vector machine (SVM) analysis. RESULTS. ...
Seventy-four patients with ODE and 48 subjects with PODE were included in the analysis. ...
The adopted learning methodology was the support vector machine (SVM), which is a supervised learning technique widely used for both classification and regression problems. 13, 14 In the specific case ...
doi:10.1167/iovs.11-8082
pmid:22110073
fatcat:umt7zrwgxrg4fitsjuogtzru7a
Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning
2021
Frontiers in Psychology
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Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods ...
The intelligent diagnostic results were compared with those of the expert diagnosis. ...
The operators were trained and qualified in a unified standardized anterior segment photographic technique. ...
doi:10.3389/fpsyg.2021.759229
pmid:34744935
pmcid:PMC8569253
fatcat:r2anaql5tfcqxlvazflbipgabu
Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network
2020
Annals of Translational Medicine
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Using deep learning techniques in image analysis is a dynamically emerging field. ...
This study aims to use a convolutional neural network (CNN), a deep learning approach, to automatically classify esophageal cancer (EC) and distinguish it from premalignant lesions. ...
LBP+SVM and HOG+SVM methods are classical machine learning methods. Compared with them, the system we presented achieved better results. ...
doi:10.21037/atm.2020.03.24
pmid:32395530
pmcid:PMC7210177
fatcat:eibngbhlvbgfxb3e3rxc334ldi
A machine-learning approach for computation of fractional flow reserve from coronary computed tomography
2016
Journal of applied physiology
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In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. ...
Invasive FFR Յ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. ...
In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. ...
doi:10.1152/japplphysiol.00752.2015
pmid:27079692
fatcat:pi4b3anbk5es7cfykyu53uvnt4
Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning
2021
Computational and Mathematical Methods in Medicine
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In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. ...
In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. ...
Learn from the statistical point of view; a machine learning task T is defined as a modeling problem of conditional probability pðy | xÞ in a domain D. ...
doi:10.1155/2021/2485934
pmid:34306173
pmcid:PMC8272675
fatcat:fztfpknyafb5vginfienpiwaaq
The evolution of ultrasound in rheumatology
2012
Therapeutic Advances in Musculoskeletal Disease
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We present a review of advances in ultrasound in rheumatology, focusing on major chronological developments. ...
Musculoskeletal ultrasound is a powerful tool not only for evaluating joint and related structures but also for assessing disease activity. ...
Conflict of interest statement The authors declare no conflicts of interest in preparing this article. ...
doi:10.1177/1759720x12460116
pmid:23227117
pmcid:PMC3512173
fatcat:mkajh4tbojefzkn3rxqqyxxrae
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