A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort.
Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There a...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 3; pp. 1 - 31 |
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| Autores principales: | , , , , , , , , , |
| Formato: | computer program diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149070804&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149070804 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2021 vid: 45 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149070804 149070804 149070804 10.1007/s10916-021-01707-w 149070804 ppf: 1 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort. aug: au: Agarwal, Mohit Saba, Luca Gupta, Suneet K. Carriero, Alessandro Falaschi, Zeno Paschè, Alessio Danna, Pietro El-Baz, Ayman Naidu, Subbaram Suri, Jasjit S. affil: CSE Department, Bennett University, Greater Noida, India sug: subj: Artificial Intelligence Italy COVID-19 Classification Severity of Illness Tomography, X-Ray Computed Lung Diseases Diagnosis Human Italy Prospective Studies Deep Learning Paradigms Machine Learning Neural Networks (Computer) Decision Trees Random Forest Descriptive Statistics Odds Ratio Physics Radiography, Thoracic Male Female Adult Middle Age COVID-19 Pandemic Aged Aged, 80 and Over Reverse Transcriptase Polymerase Chain Reaction Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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