scholarly journals A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2781
Author(s):  
Mehdi Alilou ◽  
Prateek Prasanna ◽  
Kaustav Bera ◽  
Amit Gupta ◽  
Prabhakar Rajiah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Ct Scans ◽  
Linear Discriminant ◽  
Independent Validation ◽  
Learning Classifier ◽  
Edge Sharpness ◽  
Benign Nodules ◽  
Contrast Ct ◽  
Lung Adenocarcinomas

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.

Introduction and Implementation of Machine Learning Algorithms in R

2019 ◽  
pp. 126-147
Author(s):  
S. R. Mani Sekhar ◽  
G. M. Siddesh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

scholarly journals Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans

2020 ◽  
Vol 267 (9) ◽  
pp. 2632-2641
Author(s):  
Helge C. Kniep ◽  
Peter B. Sporns ◽  
Gabriel Broocks ◽  
André Kemmling ◽  
Jawed Nawabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Posterior Circulation ◽  
Ct Scans ◽  
Posterior Circulation Stroke ◽  
Contrast Ct

scholarly journals Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

2020 ◽  
Author(s):  
A Pozzi ◽  
C Raffone ◽  
MG Belcastro ◽  
TL Camilleri-Carter
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Foramen Magnum ◽  
Sex Estimation ◽  
Learning Models ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Cranial Measurements ◽  
Machine Learning Models

ABSTRACTObjectivesUsing cranial measurements in two Italian populations, we compare machine learning methods to the more traditional method of linear discriminant analysis in estimating sex. We use crania in sex estimation because it is useful especially when remains are fragmented or displaced, and the cranium may be the only remains found.Materials and MethodsUsing the machine learning methods of decision tree learning, support-vector machines, k-nearest neighbor algorithm, and ensemble methods we estimate the sex of two populations: Samples from Bologna and samples from the island of Sardinia. We used two datasets, one containing 17 cranial measurements, and one measuring the foramen magnum.Results and DiscussionOur results indicate that machine learning models produce similar results to linear discriminant analysis, but in some cases machine learning produces more consistent accuracy between the sexes. Our study shows that sex can be accurately predicted (> 80%) in Italian populations using the cranial measurements we gathered, except for the foramen magnum, which shows a level of accuracy of ∼70% accurate which is on par with previous geometric morphometrics studies using crania in sex estimation. We also find that our trained machine learning models produce population-specific results; we see that Italian crania are sexually dimorphic, but the features that are important to this dimorphism differ between the populations.

scholarly journals Single Cell Raman Spectroscopy to Identify Different Stages of Proliferating Human Hepatocytes for Cell Therapy

2021 ◽  
Author(s):  
Chen Ma ◽  
Ludi Zhang ◽  
Ting He ◽  
Huiying Cao ◽  
Chenhui Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Cell Therapy ◽  
Linear Discriminant Analysis ◽  
Learning Model ◽  
Human Hepatocytes ◽  
Linear Discriminant ◽  
Analytical Technique ◽  
Machine Learning Model

Abstract Background: Cell therapy provides hope for treatment of advanced liver failure. Proliferating human hepatocytes (ProliHHs) were derived from primary human hepatocytes (PHH) and as potential alternative for cell therapy in liver diseases. Due to the continuous decline of mature hepatic genes and increase of progenitor like genes during ProliHHs expanding, it is challenge to monitor the critical changes of the whole process. Raman microspectroscopy is a noninvasive, label free analytical technique with high sensitivity capacity. In this study, we evaluated the potential and feasibility to identify ProliHHs from PHH with Raman spectroscopy.Methods: Raman spectra were collected at least 600 single spectrum for PHH and ProliHHs at different stages (Passage 1 to Passage 4). Linear discriminant analysis and a two-layer machine learning model were used to analyze the Raman spectroscopy data. Significant differences in Raman bands were validated by the associated conventional kits.Results: Linear discriminant analysis successfully classified ProliHHs at different stages and PHH. A two-layer machine learning model was established and the overall accuracy was at 84.6%. Significant differences in Raman bands have been found within different ProliHHs cell groups, especially changes at 1003 cm-1, 1206 cm-1 and 1300 cm-1. These changes were linked with reactive oxygen species, hydroxyproline and triglyceride levels in ProliHHs, and the hypothesis were consistent with the corresponding assay results. Conclusions: In brief, Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process. The approach can simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.

scholarly journals New approaches for predicting outcomes in patients with atrial fibrillation

2020 ◽  
Vol 27 ◽  
pp. 28-32
Author(s):  
N. A. Novikova ◽  
M. Yu. Gilyarov ◽  
A. Yu. Suvorov ◽  
A. Yu. Kuchina
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Test Group ◽  
Learning Technologies ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cardiovascular Risks ◽  
Linear Discriminant

Aim: we aimed to assess the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fi brillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classifi cation trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of importance in cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and confirmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

scholarly journals NEW METHODS FOR PREDICTING OUTCOMES AND COMPLICATIONS IN PATIENTS WITH ATRIAL FIBRILLATION

2019 ◽  
Vol 26 (2(96)) ◽  
pp. 45-50
Author(s):  
N. A. Novikova ◽  
M. Yu. Gilyarov ◽  
A. Yu. Suvorov ◽  
A. Yu. Kuchina
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Test Group ◽  
Learning Technologies ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Aim: assessment of the capabilities of “machine learning” methods in predicting remote outcomes in patients with non-valvular atrial fibrillation (AF).Methods. From 2015 to 2016 234 patients with non-valvular AF were included in the study (median age 72 (65; 79) years; 50.0% men). During the median follow-up of 2.9 (2.7; 3.2) years 42 patients died, 9 patients had non-fatal acute cerebral circulatory disorders and 3 patients had non-fatal myocardial infarction (MI). These events in 52 subjects (22.2% from all patients included) were combined into a combined endpoint (death and a nonfatal cardiovascular accident at the stage of remote observation). The first 184 patients comprised a “training” group. The next 50 patients formed the “test” group. The following methods of «machine learning» were used in the analysis: classification trees, linear discriminant analysis, the k-nearest neighbor method, support vectors method, neural network.Results. Long-term outcomes were influenced by age, known traditional risk factors for cardiovascular diseases, the presence of these diseases, changes in intracardiac hemodynamics and heart chambers as evaluated by echocardiography, the presence of concomitant anemia, advanced stages of chronic kidney disease, and the administration of drugs associated with a more severe cardiovascular disease progression (amiodarone, digoxin). The best prognosis was created using the model of linear discriminant analysis, the complex neural network model, and the support vector machine.Conclusion. Modern methods aimed at prognosis estimation seem to be of great potential for cardiology. These methods include big data analysis and machine learning technologies. The methods require further evaluation and con firmation, and in the future they may allow correcting cardiovascular risks, using data from real clinical practice and evidence-based medicine at the same time.

scholarly journals Deteksi kavitasi menggunakan linear discriminant analysis pada pompa sentrifugal

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Berli Paripurna Kamiel ◽  
Yusuf Ahmad ◽  
Krisdiyanto Krisdiyanto
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Centrifugal Pump ◽  
Shape Factor ◽  
Vibration Signal ◽  
Large Contribution ◽  
Centrifugal Pumps ◽  
Linear Discriminant ◽  
The Impact

Cavitation is a phenomenon that often occurs in the centrifugal pumps. The impact of cavitation is a decrease in pump performance which will affect the ongoing production process in the industries. It is important to have a method to detect the phenomenon of cavitation early. The vibration signal is a parameter that is often used in detecting cavitation or other faulty components. One of the methods is based on the pattern recognition i.e. machine learning. Linear Discriminant Analysis (LDA) is a machine learning algorithm that has the advantage of reducing the parameters used into low dimensions without reducing the accuracy of their classification. The study proposes LDA to classify normal conditions, initial cavitation, intermediate cavitation and severe cavitation. The recording of the vibration signal is taken using the an accelerometer mounted on the inlet of the centrifugal pump. The vibration signal is then extracted using 10 statistic parameters of time domain as the LDA feature selection, namely mean, RMS, standard deviation, kurtosis, skewness, crest factor, clearance factor, shape factor, variance and peak value. The results shows that the LDA classifier can detect and classify cavitation conditions with an accuracy rate of 98.8% on training and 99.6% on testing. The shape factor, kurtosis, skewness and RMS parameters are a combination of parameters that have a large contribution to the classifier to detect and classify cavitation conditions.Keywords: Linear Discriminant Analysis (LDA), cavitation, centrifugal pump, statistical parameter

scholarly journals Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

2018 ◽  
Vol 2672 (45) ◽  
pp. 160-172 ◽  
Author(s):  
Zuoyu Miao ◽  
K. Larry Head ◽  
Byungho Beak
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Linear Discriminant

Deployment of connected vehicles will become possible for most American cities in the next 10 to 20 years. Connected vehicle (CV) applications (e.g., mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle’s ID changes every 5 minutes, so the topic of rematching vehicles is of interest in privacy protection and performance measure research. This paper explores the possibility of rematching connected vehicles’ IDs using popular machine learning techniques, including logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear and nonlinear support vector machine (SVM) and nearest neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a software-in-the-loop technique. The best average mismatching rate is 14%. To assess potential factors’ effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measurement of future CV applications feasible and also suggest that more secure mechanisms are needed to protect the public.

Sign in / Sign up

Export Citation Format

Share Document