The “Ground-Glass” Mimicker in The Pandemic: A Novel Radiomics-Based Machine Learning Model Differentiates COVID-19 Pneumonia from Acute Non-COVID-19 Lung Disease

Abstract Ground-Glass Opacities (GGOs) are a non-specific CT finding observed in the early phase of COVID-19 pneumonia. However, GGOs are also seen in other acute interstitial and alveolar lung diseases, thus making the differential diagnosis a diagnostic challenge. In this poof-of-concept study, we aimed to differentiate COVID-19 pneumonia presenting with GGOs from acute non-COVID-19 lung disease using a novel radiomic-based model in patients who underwent a high-resolution CT (HRCT) scan at hospital admission during the first pandemic peak in Italy. HRCT scans of 28 RT-PCR diagnosed COVID-19 pneumonia (COVID) and 30 acute non-COVID-lung disease (nCOVID) were retrospectively included. All patients showed GGOs as the predominant CT pattern. Two readers, blinded to the final diagnosis, independently segmented GGOs on CT scans by using a semi-automated approach, and radiomic features were extracted from segmented images. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented to optimize the hyperparameter of PLS and to assess the model generalization. The diagnostic performance of the radiomic model to differentiate between COVID and nCOVID lung disease was assessed through receiver operating characteristic (ROC) analysis. The radiomics-based machine learning model differentiated COVID and nCOVID with an AUC = 0.868 (p = 4.2·10− 7). After a careful prospective evaluation in larger multicentric studies, it may help radiologists to rule out COVID-19 pneumonia thus improving the COVID-19 triaging in epidemic areas.

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Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

Scientific Reports ◽

10.1038/s41598-021-96755-0 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Andrea Delli Pizzi ◽

Antonio Maria Chiarelli ◽

Piero Chiacchiaretta ◽

Cristina Valdesi ◽

Pierpaolo Croce ◽

...

Keyword(s):

Machine Learning ◽

Lung Diseases ◽

Learning Algorithm ◽

Final Diagnosis ◽

Partial Least Square ◽

Least Square ◽

Ground Glass ◽

High Resolution Computed Tomography ◽

Probability Threshold ◽

Leave One Out

AbstractGround-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden’s test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden’s index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10–7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.

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MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Scientific Reports ◽

10.1038/s41598-021-84816-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Andrea Delli Pizzi ◽

Antonio Maria Chiarelli ◽

Piero Chiacchiaretta ◽

Martina d’Annibale ◽

Pierpaolo Croce ◽

...

Keyword(s):

Machine Learning ◽

Rectal Cancer ◽

Treatment Response ◽

Randomized Clinical Trials ◽

Locally Advanced ◽

Neoadjuvant Treatment ◽

Learning Model ◽

Partial Least Square ◽

Machine Learning Model ◽

Pre Treatment

AbstractNeoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

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A Multilayer Hybrid Machine Learning Model for Diabetes Detection

ITM Web of Conferences ◽

10.1051/itmconf/20203203032 ◽

2020 ◽

Vol 32 ◽

pp. 03032

Author(s):

Sahil Parab ◽

Piyush Rathod ◽

Durgesh Patil ◽

Vishwanath Chikkareddi

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Learning Model ◽

Point Of View ◽

Machine Learning Algorithms ◽

Classification Model ◽

Case Scenario ◽

Detection Model ◽

Machine Learning Model ◽

Hybrid Machine

Diabetes Detection has been one of the many challenges which is being faced by the medical as well as technological communities. The principles of machine learning and its algorithms is used in order to detect the possibility of a diabetic patient based on their level of glucose concentration , insulin levels and other medically point of view required test reports. The basic diabetes detection model uses Bayesian classification machine learning algorithm, but even though the model is able to detect diabetes, the efficiency is not acceptable at all times because of the drawbacks of the single algorithm of the model. A Hybrid Machine Learning Model is used to overcome the drawbacks produced by a single algorithm model. A Hybrid Model is constructed by implementing multiple applicable machine learning algorithms such as the SVM model and Bayesian’s Classification model or any other models in order to overcome drawbacks faced by each other and also provide their mutually contributed efficiency. In a perfect case scenario the new hybrid machine learning model will be able to provide more efficiency as compared to the old Bayesian’s classification model.

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From the Core to the Border of Locally Advanced Rectal Cancer: A Novel MRI-based Clinical-Radiomic Model Early Predicts Treatment Response

10.21203/rs.3.rs-108041/v1 ◽

2020 ◽

Author(s):

Andrea Delli Pizzi ◽

Antonio Chiarelli ◽

Piero Chiacchiaretta ◽

Martina d'Annibale ◽

Pierpaolo Croce ◽

...

Keyword(s):

Machine Learning ◽

Rectal Cancer ◽

Treatment Response ◽

Randomized Clinical Trials ◽

Locally Advanced ◽

Neoadjuvant Treatment ◽

Learning Model ◽

Partial Least Square ◽

Machine Learning Model ◽

Pre Treatment

Abstract Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (³ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC=0.793, p =5.6·10-5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

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Machine Learning Techniques to Predict Daily Rainfall Amount

10.21203/rs.3.rs-801241/v1 ◽

2021 ◽

Author(s):

Chalachew Muluken Liyew ◽

Haileyesus Amsaya Melese

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Learning Algorithm ◽

Pearson Correlation ◽

Daily Rainfall ◽

Learning Model ◽

Machine Learning Techniques ◽

Correlation Technique ◽

Learning Techniques ◽

Machine Learning Model

Abstract It is crucial to predict the amount of daily rainfall to improve agricultural productivities to secure food, and water quality supply to keep the citizen healthy. To predict rainfall, various researches are conducted using data mining and machine learning techniques of different countries’ environmental datasets. The Pearson correlation technique is used to select relevant environmental variables which are used as an input for the machine learning model of this study. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The dataset is collected from the local meteorological office to measure the performance of three machine learning techniques as Multivariate Linear Regression, Random Forest and Extreme Gradient Boost. Root mean squared error and Mean absolute Error are used to measure the performance of the machine learning model for this study. The result of the study shows that the Extreme Gradient Boost gradient descent machine learning algorithm performs better than others.

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Mitigating Insider Threats Using Bio-Inspired Models

Applied Sciences ◽

10.3390/app10155046 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5046

Author(s):

Andreas Nicolaou ◽

Stavros Shiaeles ◽

Nick Savage

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Swarm Intelligence ◽

Learning Algorithm ◽

Learning Model ◽

Security Issue ◽

Security Technology ◽

Insider Threats ◽

Optimal Subset ◽

Machine Learning Model

Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to mitigate it by developing a machine learning model based on Bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explore swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results show that swarm intelligence algorithms perform well on feature selection optimization and the generated, near-optimal, subset of features has a similar performance to the original one.

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An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging

Journal of Clinical Medicine ◽

10.3390/jcm9030658 ◽

2020 ◽

Vol 9 (3) ◽

pp. 658 ◽

Cited By ~ 1

Author(s):

Jun-Cheng Weng ◽

Tung-Yeh Lin ◽

Yuan-Hsiung Tsai ◽

Man Teng Cheok ◽

Yi-Peng Eve Chang ◽

...

Keyword(s):

Machine Learning ◽

Suicidal Ideation ◽

Learning Algorithm ◽

Area Under The Curve ◽

Learning Model ◽

Supervised Machine Learning ◽

Gradient Boosting ◽

Machine Learning Model ◽

Extreme Gradient Boosting ◽

Depressive Patients

It is estimated that at least one million people die by suicide every year, showing the importance of suicide prevention and detection. In this study, an autoencoder and machine learning model was employed to predict people with suicidal ideation based on their structural brain imaging. The subjects in our generalized q-sampling imaging (GQI) dataset consisted of three groups: 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (NS), and 58 healthy controls (HC). In the GQI dataset, indices of generalized fractional anisotropy (GFA), isotropic values of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in different machine learning models. A convolutional neural network (CNN)-based autoencoder model, the supervised machine learning algorithm extreme gradient boosting (XGB), and logistic regression (LR) were used to discriminate SI subjects from NS and HC subjects. After five-fold cross validation, separate data were tested to obtain the accuracy, sensitivity, specificity, and area under the curve of each result. Our results showed that the best pattern of structure across multiple brain locations can classify suicidal ideates from NS and HC with a prediction accuracy of 85%, a specificity of 100% and a sensitivity of 75%. The algorithms developed here might provide an objective tool to help identify suicidal ideation risk among depressed patients alongside clinical assessment.

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NIDM: Novel Intrusion Detection Model Using CNN-LSTM for Big Data Environment

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38503 ◽

2021 ◽

Vol 9 (10) ◽

pp. 827-831

Author(s):

Rohan Benhal

Keyword(s):

Machine Learning ◽

Big Data ◽

Intrusion Detection ◽

Learning Algorithm ◽

Learning Model ◽

Attack Detection ◽

Detection Model ◽

Deep Learning Algorithm ◽

Machine Learning Model ◽

Data Points

Abstract: Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection. Keywords: IDS, Machine Learning, LSTM, CNN.

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