Filters








276 Hits in 2.7 sec

REVIEW OF MACHINE LEARNING TECHNIQUES IN OPHTHALMOLOGY: A NOVEL APPROACH

Savita Bamal
2020 International Journal of Technical Research & Science  
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

María Luisa Sánchez Brea, Noelia Barreira Rodríguez, Antonio Mosquera González, Katharine Evans, Hugo Pena-Verdeal
2016 Computational and Mathematical Methods in Medicine  
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  
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

Sherry Verma, Latika Singh, Monica Chaudhry
2019 International Journal of Advanced Computer Science and Applications  
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

Xiaohang Wu, Lixue Liu, Lanqin Zhao, Chong Guo, Ruiyang Li, Ting Wang, Xiaonan Yang, Peichen Xie, Yizhi Liu, Haotian Lin
2020 Annals of Translational Medicine  
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

Nico Curti, Enrico Giampieri, Fabio Guaraldi, Federico Bernabei, Laura Cercenelli, Gastone Castellani, Piera Versura, Emanuela Marcelli
2021 Applied Sciences  
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

Robbert W. van Hamersvelt, Majd Zreik, Michiel Voskuil, Max A. Viergever, Ivana Išgum, Tim Leiner
2018 European Radiology  
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)  
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

Jia Zhang, Ruijuan Han, Guo Shao, Bin Lv, Kai Sun
2022 Journal of Personalized Medicine  
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

Arturo Carta, Stefania Favilla, Marco Prato, Stefania Bianchi-Marzoli, Alfredo A. Sadun, Paolo Mora
2012 Investigative Ophthalmology and Visual Science  
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

Wei Xu, Ling Jin, Peng-Zhi Zhu, Kai He, Wei-Hua Yang, Mao-Nian Wu
2021 Frontiers in Psychology  
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

Gaoshuang Liu, Jie Hua, Zhan Wu, Tianfang Meng, Mengxue Sun, Peiyun Huang, Xiaopu He, Weihao Sun, Xueliang Li, Yang Chen
2020 Annals of Translational Medicine  
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

Lucian Itu, Saikiran Rapaka, Tiziano Passerini, Bogdan Georgescu, Chris Schwemmer, Max Schoebinger, Thomas Flohr, Puneet Sharma, Dorin Comaniciu
2016 Journal of applied physiology  
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

Yan Wang, Zixuan Feng, Liping Song, Xiangbin Liu, Shuai Liu, Shuihua Wang
2021 Computational and Mathematical Methods in Medicine  
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

Taeyoung Kang, Stefano Lanni, Jackie Nam, Paul Emery, Richard J. Wakefield
2012 Therapeutic Advances in Musculoskeletal Disease  
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
« Previous Showing results 1 — 15 out of 276 results