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

• Published in: Journal of medical systems Vol. 45; no. 3; p. 28
• Main Authors: Agarwal, Mohit, Saba, Luca, Gupta, Suneet K., Carriero, Alessandro, Falaschi, Zeno, Paschè, Alessio, Danna, Pietro, El-Baz, Ayman, Naidu, Subbaram, Suri, Jasjit S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Artificial intelligence; Artificial Intelligence - standards; Artificial neural networks; Classification; Classification systems; Classifiers; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Decision trees; Deep learning; Deep Learning - standards; Entropy; Female; Health Informatics; Health Sciences; Humans; Imaging techniques; Italy; Learning algorithms; Learning theory; Lung - diagnostic imaging; Lungs; Machine learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Neural networks; Patient Facing Systems; Reproducibility of Results; SARS-CoV-2; Severity of Illness Index; Statistics for Life Sciences; Tomography, X-Ray Computed - methods; Transfer learning; Italy; COVID-19; Accuracy; Lung; Machine learning; Pandemic; Block imaging; Entropy; Computer tomography; Bispectrum; Tissue characterization; COVID severity index
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-021-01707-w

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.