The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia

Abstract To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

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The study of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia: A study on automatic machine learning of Radiomics

10.21203/rs.3.rs-32522/v1 ◽

2020 ◽

Author(s):

Huibin Tan ◽

Fei Xiong ◽

Yuanliang Jiang ◽

Wencai Huang ◽

Ye Wang ◽

...

Keyword(s):

Machine Learning ◽

Predictive Value ◽

Laboratory Data ◽

Texture Features ◽

Chest Ct ◽

Automatic Machine ◽

Classification Model ◽

Control Group ◽

Ct Image ◽

Severe Group

Abstract Objective: To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019(COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML).Methods: 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate(TNR), positive predictive value(PPV) and negative predictive value(NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia.Result: The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. Conclusion: The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

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Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

10.21203/rs.3.rs-785959/v1 ◽

2021 ◽

Author(s):

Lu Ma ◽

Qi Zhou ◽

Huming Yin ◽

Xiaojie Ang ◽

Yu Li ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Texture Feature ◽

Texture Features ◽

Classification Model ◽

Clinical Indicators ◽

Model Based ◽

Feature Parameters ◽

Auc Value ◽

Malignant Prostate

Abstract Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

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Correlation between Levels of Serum Lipoprotein-Associated Phospholipase A2 and Soluble Suppression of Tumorigenicity 2 and Condition of Acute Heart Failure Patients and Their Predictive Value for Prognosis

Journal of Healthcare Engineering ◽

10.1155/2021/1525190 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Jinjin Zhang ◽

Lei Wang ◽

Zhikun Zhao ◽

Liang Li ◽

Yunfeng Xia

Keyword(s):

Heart Failure ◽

Logistic Regression ◽

Regression Analysis ◽

Phospholipase A2 ◽

Acute Heart Failure ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Serum Lipoprotein ◽

Control Group ◽

Severe Group

Objective. To explore the correlation between levels of serum lipoprotein-associated phospholipase A2 (LP-PLA2) and soluble suppression of tumorigenicity 2 (sST2) and condition of acute heart failure (AHF) patients and their predictive value for prognosis. Methods. The data of patients who complained of acute dyspnea and were treated in our hospital (January 2018–January 2020) were selected for review analysis, and those diagnosed with AHF by means of chest films, physical examination, cardiogram, and color Doppler ultrasonography (CDS) were selected as the study objects. The patients were split into the mild group (I or II, 55 cases) and the severe group (III or IV, 50 cases) according to the clinical condition grading standard in Guidelines for Diagnosis and Treatment of Acute Heart Failure. In addition, 105 healthy individuals examined in our medical center in the same period were selected as the control group. The serum LP-PLA2 and sST2 levels of all study objects were measured to analyze the correlation between these levels and AHF condition. Readmission due to heart failure and all-cause death were regarded as the endpoint events, and after one year of follow-up visits, the occurrence of the endpoint events in patients of the two groups was recorded, and with the endpoint events as the variable, the patients were divided into the event group and nonevent group to establish a logistic regression analysis model and analyze the merit of serum LP-PLA2 and sST2 in evaluating patient outcome. Results. The patients’ general information such as age and gender between the severe group and the mild group were not statistically different ( P > 0.05 ), and the levels of high-sensitivity c-reactive protein (CRP), hemoglobin, creatinine, and uric acid of the severe group were greatly different from those of the mild group ( P < 0.001 ), the comparison result of serum LP-PLA2 and sST2 levels was severe group > mild group > control group ( P all <0.001), and the serum LP-PLA2 and sST2 levels of the severe group were, respectively, 275.98 ± 50.68 ng/ml and 2,122.65 ± 568.65 ng/ml; among 105 AHF patients, 50 of them had endpoint events (47.6%), including 36 in the severe group (36/50, 72.0%) and 14 in the mild group (14/55, 25.5%), and the event group presented greatly higher serum LP-PLA2 and sST2 levels than in the nonevent group ( P < 0.001 ); according to the logistic regression analysis, serum LP-PLA2 and sST2 had independent predictive value for prognosis of AHF patients, which could be used as the independent predictive factors for 1-year prognosis. Conclusion. Serum LP-PLA2 and sST2 have a good diagnosis value for the condition and prognosis of AHF patients, which shall be promoted and applied in practice.

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Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7740.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 34-40

Keyword(s):

Machine Learning ◽

Feature Fusion ◽

Learning Algorithm ◽

Texture Features ◽

Classification Model ◽

Support Vector ◽

Svm Classifier ◽

Paper Machine ◽

Textual Features ◽

Occurrence Matrix

The fruit categorization according to their visual quality has recently experienced tremendous growth in the field of agriculture and food products. Due to post-harvest loses during handling and processing, there is an increasing demand for quality products in agro industry which requires accuracy to predict the fruit. Various techniques of machine learning have been successfully applied for classifying the fruit built on binary class. In this paper, machine leaning technique is used to automate the process of categorization and to improve the accuracy of different types of fruits by feature selection. To categorized images domain specific features such as color, shape and textual features are considered. Statistical color features are extracted from the image, bounding box feature for shape features and gray-level co-occurrence matrix (GLCM) is used to extract the textual feature of an image. These features are combined in a single feature fusion. A support vector machine (SVM) classification model is trained using training set features on fruit360 dataset which includes six fruit categories (classes) with two sub category (sub-classes) which builds multiclass classification task. We present one-vs-one coding design of Error correcting output codes (ECOC) and apply to SVM classifier; validation followed a fivefold cross validation strategy. The result shows that the textual features combined with color and shape feature improved fruit classification accuracy.

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Low serum apolipoprotein A 1 is an indicator of severity in patients with coronavirus disease 2019

10.21203/rs.3.rs-33301/v1 ◽

2020 ◽

Author(s):

Yayun Yang ◽

Zhe Zhu ◽

Lingyan Fan ◽

Shuyuan Ye ◽

Kehong Lou ◽

...

Keyword(s):

Predictive Value ◽

Risk Model ◽

Laboratory Data ◽

Density Lipoprotein ◽

Blood Lipid ◽

Apolipoprotein A1 ◽

Lipoprotein Cholesterol ◽

Predictive Values ◽

Significant Difference ◽

Severe Group

Abstract Background: Recently, dyslipidaemia was observed in patients with coronavirus disease 2019 (COVID-19), especially in severe cases. This study aimed to explore the predictive value of blood lipid levels for COVID-19 severity.Methods: All patients with COVID-19 admitted to HwaMei Hospital, University of Chinese Academy of Sciences, from January 23 to April 20, 2020, were included in this retrospective study. General clinical characteristics and laboratory data (including blood lipid parameters) were obtained, and their predictive values for the severity were analysed.Results: In total, 142 consecutive patients with COVID-19 were included. The non-severe group included 125 cases, and 17 cases were included in the severe group. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) at baseline were significantly lower in the severe group. ApoA1 and interleukin-6 (IL-6) were recognized as independent risk factors for COVID-19 severity. ApoA1 had the highest area under the receiver operator characteristic curve (AUC) among all the single markers (AUC: 0.896, 95% CI: 0.834-0.941). Moreover, the risk model established using ApoA1 and IL-6 enhanced the predictive value (AUC: 0.977, 95% CI: 0.932-0.995). On the other hand, ApoA1 levels were elevated in the severe group during treatment, and there was no significant difference between the severe and non-severe groups during the recovery stage of the disease.Conclusion: The blood lipid profile in severe COVID-19 patients is quite different from that in non-severe cases. Serum ApoA1 could severe as a good indictor to reflect the severity of COVID-19.

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Developing a Primary Care EMR-based Frailty Definition using Machine Learning

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.811 ◽

2018 ◽

Vol 3 (4) ◽

Cited By ~ 1

Author(s):

Sylvia Aponte-Hao ◽

Bria Mele ◽

Dave Jackson ◽

Alan Katz ◽

Charles Leduc ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Positive Predictive Value ◽

Predictive Value ◽

Laboratory Data ◽

Screening Tools ◽

Geriatric Syndrome ◽

Surveillance Network ◽

Clinical Frailty Scale ◽

Definition Of

IntroductionFrailty is a geriatric syndrome that is predictive of heightened vulnerability for disability, hospitalization, and mortality. Annually an estimated 250,000 frail Canadians die, and this estimate is expected to double in the next 40 years, as Canadians grow older. Currently there is no single accepted clinical definition of frailty. Objectives and ApproachThe objective of this study was to develop an operational definition of frailty using machine learning that can be applied to a primary care electronic medical record (EMR) database. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a pan-Canadian network of primary care practices that collect de-identified patient information (such as encounter diagnoses, health conditions, and laboratory data) from EMRs. 780 patients from CPCSSN have were randomly selected and assessed by physicians using the Rockwood Clinical Frailty Scale (as frail or not frail), and their clinical characteristics from CPCSSN used to develop the definition using machine-learning. ResultsA total of 8,044 clinical features were extracted from these tables: billing, problem list, encounter diagnosis, labs, medications and referrals. A chi-squared automatic interaction detector (CHAID) approach was selected as the best approach. The bootstrapping process used a cost matrix that prioritized high sensitivity and positive predictive value. 10-fold cross validation was used for validity measures. Key features factored into the algorithm included: diagnosis of dementia (ICD-9 code 290), medications furosemide and vitamins, and use of key word “obstruction” within the billing table. The validation measures with 95% confidence intervals are as follows: sensitivity of 28% (95% CI: 21% to 36%), specificity of 94% (95% CI: 93% to 96%), positive predictive value of 53% (95% CI: 42% to 64%), negative predictive value of 86% (95% CI: 83% to 88%). Conclusion/ImplicationsNo other primary care specific frailty screening tools have sufficient validity. These results suggest heterogeneous diseases require clearly defined features and potentially more sophisticated algorithms to account for heterogeneity. Further research utilizing continuous features and continuous frailty scores may be more suitable in the creation of a case detection algorithm.

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Machine learning is the key to diagnose COVID-19: a proof-of-concept study

Scientific Reports ◽

10.1038/s41598-021-86735-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cedric Gangloff ◽

Sonia Rafi ◽

Guillaume Bouzillé ◽

Louis Soulat ◽

Marc Cuggia

Keyword(s):

Machine Learning ◽

False Negative ◽

Laboratory Data ◽

Chest Ct ◽

Potential Contribution ◽

Imaging Data ◽

Learning Models ◽

Rt Pcr ◽

Negative Results ◽

Machine Learning Models

AbstractThe reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.

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A Hybrid Approach for Automated Plant Leaf Recognition Using Hybrid Texture Features and Machine Learning-Based Classifiers

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2021070103 ◽

2021 ◽

Vol 13 (2) ◽

pp. 27-39

Author(s):

Upendra Kumar ◽

Shashank Yadav ◽

Esha Tripathi

Keyword(s):

Machine Learning ◽

Hybrid Approach ◽

Texture Features ◽

Classification Model ◽

Automated System ◽

Support Vector ◽

Feature Extraction Method ◽

Plant Recognition ◽

Low Efficiency ◽

Ann Classifier

Automated plant recognition performs a significant role in various applications used by environmental experts, chemists, and botany experts. Humans can recognize plants manually, but it is a prolonged and low-efficiency process. This paper introduces an automated system for recognizing plant species based on leaf images. A hybrid texture and colour-based feature extraction method was applied on digital leaf images to produce robust feature, and a further classification model was developed. A combination of machine learning methods, such as SVM (support vector machine), KNN (k-nearest neighbours), and ANN (artificial neural network), was applied on dataset for plant classification. This dataset contains 32 types of leaves. The outcomes of this work proved that success rate of plant recognition can be enhanced up to 94% with ANN classifier when both shape and colour features are utilized. Automatic recognition of plants is useful for medicine, foodstuff, and reduction of chemical wastage during crop spraying. It is also useful for identification and preservation of species.

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Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study

Frontiers in Medicine ◽

10.3389/fmed.2021.768467 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hamid Reza Marateb ◽

Farzad Ziaie Nezhad ◽

Mohammad Reza Mohebian ◽

Ramin Sami ◽

Shaghayegh Haghjooy Javanmard ◽

...

Keyword(s):

Ct Scan ◽

Predictive Value ◽

Gold Standard ◽

Chest Ct ◽

Control Group ◽

Symptom Duration ◽

Rt Pcr ◽

Blood Tests ◽

Diagnosis Method ◽

Chest Ct Scan

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

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Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

Frontiers in Digital Health ◽

10.3389/fdgth.2021.662343 ◽

2022 ◽

Vol 3 ◽

Author(s):

Luís Vinícius de Moura ◽

Christian Mattjie ◽

Caroline Machado Dartora ◽

Rodrigo C. Barros ◽

Ana Maria Marques da Silva

Keyword(s):

Machine Learning ◽

Random Search ◽

Texture Features ◽

Classification Model ◽

Recursive Feature Elimination ◽

X Rays ◽

X Ray ◽

Reverse Transcription Pcr ◽

Ensemble Machine Learning ◽

Chest X Ray

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

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