A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

Abstract

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Keywords: ARDS; Artificial intelligence; COVID-19; CT; Comorbidity; Deep learning; Machine learning; Medical imaging; Transfer learning; US; Ultrasound; X-ray.

Fig. 1

Total confirmed cases per million as of December 21, 2020 [15]. (Source: Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Maryland, USA.).

Fig. 2

Images of COVID-19 infection: (a) lung ultrasound (hyper-echoic region of the COVID-19 lung), (b) chest X-rays (the infected region in the lung), and (c) lung CT (segmented lung region; courtesy of Luca Saba, University of Cagliari, Italy). (d) The number of COVID-19 studies involving ARDS, ML, TL, DL, validation, data acquisition (DA), and 3-D imaging.

Fig. 3

The flowchart showing the research strategy.

Fig. 4

The pathophysiology of ARDS after COVID-19 infection, which consists of six phases: (i) inflammatory phase, (ii) dilatation phase, (iii) edematous phase, (iv) alveolar collapsing phase, (v) gas-exchange disorder, and (vi) hypoxemia. (Courtesy of AtheroPoint™, Roseville, CA, USA; reproduced with permission.)

Fig. 5

The number of subjects enrolled in the ARDS-based studies that consider comorbidities.

Fig. 6

Depiction of comorbidities collected from 48 studies.

Fig. 7

Mortality due to the age factor (in years) with comorbidities in the cohort from the selected studies.

Fig. 8

An online ML-based COVID-19 risk prediction system. (Courtesy of AtheroPoint™, Roseville, CA, USA; reproduced with permission.)

Fig. 9

A custom CNN-based DL architecture comprising different layers.

Fig. 10

An example of transfer learning (TL) architecture using VGG16.

Fig. 11

(a) An X-ray scanner. (b) A CT-scanner (Courtesy of Luca Saba, University of Cagliari, Italy). (c) Studies using CT vs. X-ray.

Fig. 12

X-ray scans of COVID-19, pneumonia, and normal lungs (Reproduced with permission [219]).

Fig. 13

CT scans classified as positive for coronavirus abnormalities and their corresponding color heatmaps (Reproduced with permission [220]).

Fig. 14

A 3-D graph representing the relationship between CNN layers, data augmentation, and accuracy. (Courtesy of AtheroPoint™, Roseville, CA, USA; reproduced with permission [14].)

Fig. 15

Microscopic views of (a) interstitial pneumonia and (b) COVID-19 pneumonia. (Courtesy of Luca Saba, A.O.U., Cagliari, Italy.)

Fig. 16

Three lungs with non-COVID-19 pneumonia (a1, a2, and a3). Three lungs with COVID-19 pneumonia with different COVID-19 severities (b1, b2, and b3). (Courtesy of AtheroPoint™, Roseville, CA, USA; reproduced with permission.)

Fig. 17

Bispectrum analysis of non-COVID-19 pneumonia (NCoP) and COVID-19 pneumonia (CoP). (Courtesy of AtheroPoint™, Roseville, CA, USA; reproduced with permission.)