key: cord-0879369-d0zcbz4g
authors: Suri, Jasjit S.; Agarwal, Sushant; Gupta, Suneet K.; Puvvula, Anudeep; Biswas, Mainak; Saba, Luca; Bit, Arindam; Tandel, Gopal S.; Agarwal, Mohit; Patrick, Anubhav; Faa, Gavino; Singh, Inder M.; Oberleitner, Ronald; Turk, Monika; Chadha, Paramjit S.; Johri, Amer M.; Sanches, J Miguel; Khanna, Narendra N.; Viskovic, Klaudija; Mavrogeni, Sophie; Laird, John R.; Pareek, Gyan; Miner, Martin; Sobel, David W.; Balestrieri, Antonella; Sfikakis, Petros P.; Tsoulfas, George; Protogerou, Athanasios; Misra, Durga Prasanna; Agarwal, Vikas; Kitas, George D.; Ahluwalia, Puneet; Teji, Jagjit; Al-Maini, Mustafa; Dhanjil, Surinder K.; Sockalingam, Meyypan; Saxena, Ajit; Nicolaides, Andrew; Sharma, Aditya; Rathore, Vijay; Ajuluchukwu, Janet N.A.; Fatemi, Mostafa; Alizad, Azra; Viswanathan, Vijay; Krishnan, P.K.; Naidu, Subbaram
title: A Narrative Review on Characterization of Acute Respiratory Distress Syndrome in COVID-19-infected Lungs using Artificial Intelligence
date: 2021-01-18
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2021.104210
sha: 2d2026f074774c7bb0e1e9730b50238537628cc1
doc_id: 879369
cord_uid: d0zcbz4g

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.

In December 2019, the novel coronavirus, severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) [1] , appeared in Wuhan, in the Hubei province of the People's Republic of China. The disease caused by the virus was initially called novel coronavirus pneumonia (NCP) by the Chinese government [2] . Even though it has been argued that this disease is syndemic 1 (has both biological and social factors) [3] , it was subsequently renamed the coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). It is currently a global pandemic [4] . It is a respiratory disease that can lead to acute respiratory distress syndrome (ARDS) and eventually to death. As of December 21, 2020, more than 77.4 million COVID-19 cases have been reported, causing 1.7 million casualties [5] . COVID-19 has a Ro value between 2.43 to 3.10, meaning that it is highly contagious [6] . The colors in Figure 1 (from white to dark red) show the number of cases reported per million of population worldwide. The eight countries with the highest mortality are the USA, Brazil, India, Mexico, the UK, Italy, France, and Spain [7] .

Genetically, COVID-19 is closer to SARS-CoV-1 than to Middle East respiratory syndrome coronavirus (MERS-CoV). However, it differs from SARS-CoV-1 in the length of the incubation period, clinical severity, and transmissibility [8] . Despite government initiatives to implement social habits such as social distancing and wearing masks, and despite quarantining and nonpharmacological and preventive treatments for psychophysical wellbeing, the global spread of COVID-19 has increased [9] [10] .

COVID-19 has unique imaging characteristics that constitute a visual identity when viewed through a radiologist's lens. Furthermore, COVID-19 adversely affects organs other than the lungs [11] , [12] . Due to these circumstances, research on this topic has exploded, with nearly 72,000 COVID-19-related articles published since December 2019 (see PubMed website [13] ). This translates to an average of 2,000 articles per week. Interestingly, more than 900 articles are on COVID-19 and artificial intelligence (AI); these articles cover the models of machine learning (ML), transfer learning (TL), and deep learning (DL) (see Figure 2 (d) ). It appears that AI can play a role in 1 Quotation from Wikipedia [https://en.wikipedia.org/wiki/Syndemic]: "A syndemic or synergistic epidemic is the aggregation of two or more concurrent or sequential epidemics or disease clusters in a population with biological interactions, which exacerbate the prognosis and burden of disease." J o u r n a l P r e -p r o o f the characterization of ARDS in the lungs and the characterization and diagnosis of other parts of the body [14] . The topic of AI for ARDS characterization must be investigated carefully to help practitioners better manage viral COVID-19 pneumonia leading to ARDS. AI is involved in many facets of COVID-19 management, such as medical imaging, risk assessment, telemedicine, and patient follow-up [16] . Even though AI has penetrated into several fields of radiological imaging [17] , such as classification of images into (a) controls, (b) community viral pneumonia, and (c) COVID-19 pneumonia [18] [19] [20] , the question that remains unanswered is whether these models actually function the way the pulmonologists and critical care physicians want. For example, if the patient has a specific pre-existing disease, can an AI include this information into its knowledgebase and use it to more effectively correlate this comorbid condition, the age factor, and the grayscale patterns in lung scans with COVID-19 severity? Furthermore, there are other questions worth addressing, such as the following: (i) What kind of imaging modalities are useful for ARDS? (ii) What kind of image-based classifiers are most beneficial for detecting and classifying ARDS severity while also considering comorbidity, age group, and imaging characteristics? (iii) How can COVID-19 severity due to ARDS be measured? (iv) How should we J o u r n a l P r e -p r o o f estimate the survival of patients affected by ARDS? (v) How does a pre-existing disease or comorbidity affect the mortality from ARDS? (vi) What methods (new/hybrid) can be used to detect and classify the early stages of ARDS? All these issues must be addressed if valid diagnoses are to be given and if the therapeutic applications of the AI framework are to be evaluated accurately.

The most popular medical imaging tools used for lung imaging are ultrasound, X-rays, CT (see J o u r n a l P r e -p r o o f

The layout of the study is as follows. The research strategy is presented in section 2. The pathophysiology of ARDS is described in section 3. The effect of comorbidity with COVID-19 is analyzed in section 4. In section 5, AI architectures are divided into schools of thought (SoT). The practical aspect of AI for COVID-19 is explained in section 6. The critical discussion and conclusion are given in sections 7 and 8, respectively. Figure 3 shows the flowchart of our research strategy. We used four online research databases:

PubMed, IEEE Xplore, ArXiv, and Web of Science. Initial screening used the keywords "COVID-19," "coronavirus," or "ARDS," with the modality terms "lung CT," "X-ray," or "ultrasound." The search was augmented with terms "artificial intelligence," "machine learning," or "transfer learning,"

J o u r n a l P r e -p r o o f 80% genetic resemblance between SARS-CoV-1 and SARS-CoV-2. It has been shown via molecular pathways [26] that SARS-CoV-2 has a high potential for binding with AT2 cells in the lungs [27] .

In the inflammatory phase (marked as 3), inflammatory mediators are secreted as a systemic inflammatory response to the infection of AT2 cells by SARS-CoV-2 on the alveolar epithelium surface [28] . The secreted inflammatory mediators stimulate alveolar macrophage, producing cytokines like IL-1, IL-6, and TNF-α. In turn, the elevated production of cytokines and chemokines results in a condition called a cytokine storm. The sequence of the systemic inflammatory response, the cytokine storm, and the failure of multiple organs significantly influences ARDS's pathophysiology. These sequences can also be seen in all other viruses belonging to all CoV families (e.g., SARS-CoV-1 and MERS-CoV) [29] [30] . This leads to the upregulation of trypsin.

Furthermore, it damages the endothelial tight junction protein and the zonula occludens, disorganizing and weakening endothelial cells and increasing these cells' vascular permeability to intravascular fluids [31] . In the dilatation phase (marked as 4), the cytokine storm causes endothelial barrier dysfunction. This is caused by the release of the cytokines following a viral infection. In the edematous phase (marked as 5), intravascular permeability leads to the diffusion of intravascular fluid (proteins, neutrophils, erythrocytes, and platelets) to the sub-alveolar and interstitial space, thereby causing diffused alveolar and interstitial exudates, as well as alveolar edema. At this point, one can observe radiological consolidations using lung CT as a diagnostic approach, and monitor the treatment prognosis [32] . Diffused alveolar and interstitial exudates and increased sub-alveolar edema are the hallmark features of ARDS [33] .

During the alveolar collapsing phase (marked as 6), increased tension (due to the drowning of alveoli in the fluid accumulated in the sub-alveolar and interstitial space) causes alveolar collapse, which results in the disruption of gas exchange from alveoli [34] . The alveolar gas-exchange disorder (marked as 7) occurs because of the ventilation-to-perfusion mismatch between carbon dioxide and oxygen. This condition leads to hypoxemia and ARDS (marked as 8). This leads to increased mortality if left untreated [35] . Figure 5 shows the number of subjects with comorbidities enrolled J o u r n a l P r e -p r o o f in the ARDS-based studies.

We used the following criteria during the selection process. The chosen keywords were hypertension, diabetes, obesity, chronic kidney disease, cardiovascular diseases, liver disease, hyperlipidemia, renal dysfunction, cancer, human immunodeficiency virus (HIV), lung disease, and cerebrovascular disease. The percentage of comorbidity subjects is shown in the pie chart as depicted in Figure 6 . This was designed using the selected 48 studies. Note that these 48 studies were pure medical journals discussing the role of comorbidity ("pre-existing diseases") and "age groups".

Therefore, we decided to only consider these studies as a rationale and motivation for statistical data collection, which was likely to be helpful in the design of the innovative AI solutions for COVID-19

severity diagnosis and monitoring. The two most important contributing factors to COVID-19 were diabetes and hypertension, which together account for about 68%. The rest of the comorbid predictors were nearly the same, in the range of 4%-13%. The selected studies stated that a total of 14% deaths were due to comorbidities in the ARDS framework. This pie chart demonstrates that comorbidity plays a vital role in the mortality of each cohort. Figure 7 shows the distribution of the death due to the age factor with at least one comorbidity in the cohort. The most affected age range is 66-69, with a total coverage of 58% of the total cohort, followed by age >70 with total of 20% from the selected studies.

All these subjects had COVID-19 with one or more of these comorbidities. These comorbidities all contributed to the worsening of COVID-19. Thus, each comorbidity represents a category of the cohort whose imaging characteristics (or grayscale features) are likely to be similar. Therefore, multiple knowledge-based AI systems can be developed for each comorbidity using independent training cohorts. Based on these independently trained systems corresponding to each comorbidity, lung scans be characterized and COVID-19 severity can be assessed.

J o u r n a l P r e -p r o o f

Characterization Definition. The concept of the characterization of the diseases using AI has been adopted in nearly all medical imaging areas. This includes the AI's role in pinpointing the disease, extracting the ROI of the disease, and automatically classifying the disease against binary or multiclass events. We have chosen machine learning and deep learning characterization systems that overlap and synchronize with ARDS frameworks. The idea of using this characterization system is to share the origination and innovation spirits derived for different modalities and for different organs or applications. Note that some of these are taken from our own group intentionally to show the role of tissue characterization using AI or disease characterization using AI models. These systems are the crux solutions for diversification, which are unique and not available elsewhere. We specifically listed these as a "disease-specific portfolio" as a "one-stop-shop" to focus the search for the young scientists who are particularly interested in knowing how ARDS paradigms are related to other organ paradigms. Lastly, the third advantage of this cluster is to directly approach our group should more intricate details are necessary for such parallel characterization systems. Examples of AI-based characterization can be seen in several other body and disease applications, such as brain [ [136] . Note that we excluded (a) lung cellular images and (b) animal studies by considering only human lungs. Further state-of-the-art 2017-2020 studies were only considered for non-ARDS models, and validation and inter-and intra-observer variability studies were also not considered in Table 1 .

It is essential to note that there are three typical components of an AI-based ARDS system: (a) segmentation of the lung region, (b) classification component, and (c) COVID-19 severity measurement (see Table 2 ). For the ARDS framework in this narrative review, we have proposed and classified the literature based on architecture (or so-called school of thought (SoT)) (see Table 3 ).

the AI architecture has been central to different engineering applications. Imaging has been the most prominent AI application. Recently, Biswas et al. [17] presented a class of AI architectures for medical imaging. In our current study we present several applications using different classes of architectures (SoT). Biswas also discussed the evolution of architectures in terms of the present and future of deep learning [16] . Table 1 . AI-based studies involved during Non-ARDS and ARDS periods.

AI-based Non-ARDS: AI on ARDS lung data during pre-COVID-19.

AI-based ARDS: AI on ARDS lung data postmarked December 2019 COVID-19 (post-COVID-19). [158] [159] [162] These architectures can use the manual or automated feature selection method. Typically, manual feature selection methods were used in the past, where the features were hand-picked and fed to the classification model during its learning and training process. These were older generation methods. In other words, this is an old school of thought. Since the evolution of the concept of mimicking the brain using a greater number of layers for filtering the features, deep learning emerged. The main characteristic of deep learning (DL) paradigms is the ability to compute millions of training parameters automatically. This is a more recently evolved subset of ML. These methods are another, more recent school of thought. Thus, the schools of thought are primarily defined by their foundational architecture.

Although the AI industry is dominated by these two paradigms, it continues to look for alternatives. DL begins to look outdated, and more advanced architecture has started to emerge. For example, the challenges of DL during training have led to generating pre-trained weights to speed up and simplify the DL systems. This type of architecture is known as transfer learning. It is considered a more recent SoT and a classic in its category. In summary, the classification of the architectural has been synchronized more as a SoT based on the ingredients and components of the architecture. This is similar to the way segmentation of images many years ago was categorized into several architectures or SoT. For example, segmentation can be classified as region-based, contour-based, or knowledgebased. The categories were then fused together to generate intermediate architectures. These were known as fused architectures, and they combined region and contour or region with knowledge, generating another architectural paradigm or SoT. This can be seen in the classic papers by Suri [176] [177] [178] .

Therefore, a SoT is a synonym for architectural design, where the individual and fused architectures are each considered a SoT. Fused architectures have been developed by different groups J o u r n a l P r e -p r o o f around the globe and are presented in the literature. Thus, the SoT is a more refined version of an architectural design. In summary, the idea of the School-of-Thought it to make it more granular rather than being too binary. Some of our previous studies have used the school of thought concept. Please note the SoT are technically the same as the generations of architectures; however, they give more subtle differences.

The AI models used in different SoT along with their salient features are listed below.

SoT-1 is useful for obtaining a quantitative measurement of the extent of COVID-19 lung damage and classifying the lung within the binary and multiclass frameworks. Lung segmentation is automated without human intervention, using either commercial software, such as XMedCon [179], or AI techniques, such as threshold segmentation [180] or UNet-based segmentation [146] . The AI component is state-of-the-art or custom deep learning (DL) architecture. 30  41  47  50  52  66  82  85  136  143  147  151  183  200  201  225  287  299  300  312  339  385  410  416  463  515  684  799  845  978  1, The purpose of SoT-2 is similar to that of SoT-1 (to quantitatively and categorically estimate COVID-19 severity). However, researchers have employed a hybrid architecture that is more complex than the architecture of SoT-1. the lungs in medical images, as shown in [146] and [167] . An online ML-based COVID-19 risk prediction system is depicted in Figure 8 . This is very much along the lines of previous ML systems published by our group [165] [182] [183] .

A reference DL architecture created using [184] is shown in Figure 9 . TL is an extension of state-of-the-art DL architectures that are pre-trained using massive datasets.

TL techniques produce more accurate results when there is less training data, when the available hardware has fewer capabilities, or when little training time is available. The reference architecture of TL created using [185] (called VGG16) is shown in Figure 10 . The "Highlight/Objective" attribute represents the distinguishing feature of the research. The "Architecture Description" attribute describes the underlying base AI architecture adapted in the study. The "Performance Metrics" attribute shows which metrics the researchers had used to validate their work. Readers are encouraged to look into dedicated AI-based reviews for a deeper understanding of AI and its applications.

The three chest imaging modalities X-ray, CT, and ultrasound have been strongly recommended by WHO for the diagnosis of COVID-19 [90] . In their guidelines, the following observations were given for COVID-19 diagnosis. (i) ) X-rays was found to be lower in sensitivity and higher specificity than chest CT imaging [188] [189] . (ii) Chest X-rays are less expensive, have lower radiation, takes less acquisition time, and are less expensive to use for monitoring than CT. (iii) Chest CT was found to have higher sensitivity but lower specificity and emits more radiation than X-ray. (iv) Lung [191] imaging is another high-level imaging technique that was used in many studies to diagnose COVID-19 [95] [96] . PET/CT is a more advanced imaging technique than CT alone; however, it is more expensive [23, 192] . We did not find any relevant studies for the diagnosis of COVID-19 with FDG-PET/CT imaging that used AI. The suitability of X-ray and CT imaging for COVID-19 (see Figure   11 ), based on recommendations by WHO, is shown in Table 4 , where we discuss the workflow and the practical implementation. The major studies for automated COVID-19 diagnosis using the AI paradigm with X-rays and CT are [193] [202] [192] . The 5 th attribute (type of AI models) includes two main categories, ML-and DL-based studies. In J o u r n a l P r e -p r o o f one study on chest X-rays, images were classified as normal, pneumonia, other diseases, or COVID-19 using a DL-based network, with an accuracy of 90.13% [219] . Some of the images and their corresponding color maps are shown in Figure 12 . In another study, COVID-19 abnormalities were detected in lung CT scans using a DL-based network, and a heatmap of corresponding severity levels was generated [220] . The resultant images are shown in Figure 13 . For the 6 th attribute (image augmentation), data limitation is always a big challenge for medical studies. Therefore, most of the studies used TL with DL models in such scenarios, especially when data is limited (i.e., in the thousands). Due to limited data, augmentation was applied to increase the data synthetically [210] In each round, the training is performed, and the model predicts the outcome based on the test sample. These predictions are compared against the gold standard to compute the ROI and performance of the AI system. Some authors ( [203] , [205] , [215] , and [218] ) used five-fold CV, and one author [207] used ten-fold CV. The hardware constraints (the 8 th attribute) is discussed further in the critical discussion section. During optimization (the 9 th attribute) of the AI model, it is important to avoid overfitting. CV and TL were used to suppress over-fitting in supervised learning. Overfitting is a situation where the model can predict known data accurately, but is unable to predict unknown data. There are many ways to avoid overfitting; one of them is to use more data in the training model.

The performance (the 10 th attribute) of the classifier depends on various factors, such as the number of subjects, the sample size, the training iterations, the training time, etc. Therefore, we cannot identify a single best-performing model or method at this stage. However, over time, as more studies and trials are conducted, the superior AI models will emerge.

Clinical Requirements for COVID-19 AI Systems. An ideal AI system for COVID-19 must be robust and stable, and its output must vary within the acceptable limits with changes in the demographics or J o u r n a l P r e -p r o o f other patient-related characteristics. It must be reliable and reproducible; i.e., it must yield similar results across multiple trials. Furthermore, when used in an operating room, it must be reasonably fast and cost-effective compared to traditional COVID-19 diagnostic techniques (e.g., real-time polymerase chain reaction (RT-PCR) tests). The AI system must be generalized by being trained and tested on an equal percentage of data. COVID-19 severity is a critical metric for doctors treating COVID-19 patients, and it is a desirable requirement of any AI diagnostic system.

Radiologists, pulmonologists, and doctors must validate the results of the AI system before its effectiveness can be determined. Finally, the AI system should segment the impact of COVID-19 on different organs using 3-D imaging to further assist clinicians. There is a vast potential for improvement for existing SoT using the AI-assisted diagnostics of COVID-19 patients by incorporating the above clinical requirements.

System Optimization. The majority of AI-based systems for COVID-19 detection and classification are based on DL. Several researchers have used data augmentation to increase the volume of COVID-19 data for training. The number of convolution layers is a hyperparameter that researchers need to adjust based on their intuition. However, DL-focused AI systems for COVID-19 need to be optimized based on the relationship between augmentation, the number of convolution layers, and classification accuracy ( Figure 14) .

Scientific Validation. The behavior of an AI-based COVID-19 diagnostics system should be observed and validated under various comorbidity conditions. There are numerous scenarios in which the underlying assumptions may change. For example, the CT scan's thickness can be altered, or a different view of a CT scan (coronal, sagittal, and axial) can be given to the system for diagnostic purposes. Furthermore, the stability of a system must be validated by changing the combination of data that is used (e.g., K10, K5, K2, the so-called partition protocol). A stable AI system will yield a minimum standard deviation value across different data combinations [165] . The system should also be validated for patients from different age groups and with different comorbidities.

Clinical Validation. In order for the research to be accepted by the regulatory authorities, a gold standard validation should be performed through a physical examination of the body organs against the results predicted by the AI system. One way of doing this is through the microscopic validation of the body tissues for the severity of ARDS from COVID-19, as shown in Figure 15 . Inter-observer and intra-observer variability analysis [223] should also be performed to minimize any human bias in the system. AI for COVID-19 Comorbidity and Age-Group Frameworks. The AI system must be designed to accommodate the patients' comorbidities (e.g., diabetes, cardiovascular diseases, obesity, retinopathy, pancreatic diseases, blood vessel diseases, and angiography [224] ), along with their age groups. In [225] , an AI system was developed to predict cardiovascular diseases in a multi-ethnic patient scenario. The AI systems designed in [226] and [227] can be extended to COVID-19 diagnosis by including comorbidities and age groups.

The primary AI system can be broken down into multiple subsystems for various comorbidity classes, with each comorbidity class having further subclasses for different age groups. For instance, if there are five comorbidities and three age groups, there will be 15 subsystems. Each subsystem should be trained separately using a different gold standard database [228] . The appropriate AI subsystem for a new patient can be determined by feeding additional input information about the patient's comorbidities and age group into the system. CT: [229] [147] [148] X-ray: [149] [151] LUS: [150] 3-D: [229] [147] [148] 2-D: [149] [150] [151] Multiview fusion [229] , Multiview pyramid network with attention [147] , training using human in loop [148] , videobased real time prediction [150] , end-to-end DL architecture for semi quantitative prediction COVID-19 severity [151] Resnet50 [229] , Custom CNN with attention [147] , VB-Net [148] , commercial deep learning system by Lunit Inc [149] , Spatial Transformer Network [150] , ensemble of multiple networks (Backbone -ResNet, VGG, DenseNet, Inception; Segmentation-UNet, UNet++; Alignment-Spatial Transformer Network; Scoring Head-Feature Pyramid Network; Custom Network) [151] ACC: [229] [147] [148] [150] [151] AUC: [229] [147] Sensitivity: [229] [149] [150] Specificity: [229] [149] Others: [148] [149] [150] [151] SoT-2 [152] [153]

CT: [152] [153] X-ray: LUS:

3-D: [153] 2-D: [152] Biomarker based model [152] , model for severity in 3-D lung abnormalities [153] Resnet34 with logistic regression [152] , Dense UNet [153] AUC: [152] Others: [152] [153]

SoT-3 3-D Convolution Network [154] , multi-objective differential evolution based CNN [171] , comparison of ten CNNs [156] , weakly supervised DL model [173] , truncated InceptionNet [174] , modified DarkNet CNN [157] Resnet50 [154] SoT-4 [157] CT: [157] X-ray: NA LUS: NA 3-D: [157] 2-D: NA ML and DL hybrid network for classification and prognosis [157] Resnet18 with Gradient Boosting [157] ACC: [157] AUC: [157] SoT-5 [158] [159]

CT: [158] X-ray: NA LUS: [158] 3-D:NA 2-D: [158] [159]

Pleural line identification using ML [158] , automatic severity assessment and exploration of severity related features using ML [159] Hidden Markov Model and Viterbi Algorithm combined with SVM [158] , Random forest [159] ACC: [158] [159] AUC: [[159] Sensitivity: [158] Specificity: [158] Others: [158] Multi-Modality Data. Radiography provides many solutions for diagnosing COVID-19, like lung CT, chest X-rays, lung ultrasound, and PET/CT scans. The choice of modality depends on the patient's comorbidities, age, and pre-test probabilities (PTPs) [18] . A PTP test on troponin levels (which is an indirect indicator of hypoxia) can be conducted to identify the severity of a COVID-19 infection. During the initial phase of the COVID-19 infection, X-rays and lung ultrasounds can be useful owing to their low cost and easy obtainability, and to the small footprint of the medical device. However, if a patient has comorbidities, is elderly, or has COVID-19-induced ARDS (which leads to hypoxia), lung CT is likely the best modality due to its higher resolution and robust diagnostic capabilities. Any AI system for COVID-19 must be adaptable and generalizable to multiple modalities according to each patient's requirements. [14] .

This study provides an insight into acute respiratory distress syndrome; into its relationship to comorbidity; into medical imaging modalities for imaging COVID-19 subjects in an ARDS framework; into the workflow and practical aspects of premier imaging tools; into the design of AI architectures and their adaptation to handle ARDS-based lung severity diagnoses; and finally, into the role of AI-based solutions for comorbid conditions. The study also highlights the critical components needed for a safe and effective AI-based paradigm for risk assessment of COVID-19 severity.

Even though the study offers several new directions through the amalgamation of comorbiditybased AI designs, it is missing several other facets that could be included in a more compressive review. These include the biological processes that inherit comorbidities. This omission is mainly due to limitations of manuscript length. More stringent comparisons of deep learning paradigms can be adapted; we have provided leads to previously published dedicated reviews on AI. [238] . This fundamental study can also be further developed in several other directions, covering the fields of neurology [239] , cardiology, diabetology [240] , and ophthalmology [241] . This includes the applications of statistical tools for more systematic review and meta-analysis (SRMA). Since this is a narrative review, the SRMA is beyond its scope. 

This study is one of the latest contributions that investigated different kinds of comorbidities, its contributions to the ARDS-based framework, its effect on mortality, and finally, the study proposed the AI-based solutions using comorbidity as an independent factor in their design. The salient features of the seven types of school-of-thought were presented, and their architectural characteristics were highlighted. We conclude that there are critical components of the AI system that must be thoroughly investigated before it can be applied clinically for diagnosis of COVID-19 severity. These include ( 

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PhD Editor-in-Chief Computers

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.We understand that the Corresponding Author is the sole contact for the Editorial process. He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from." I, Mainak Biswas on behalf of all authors of the manuscript "A Narrative Review on Characterization of Acute Respiratory Distress Syndrome in COVID-19 Lungs using Artificial Intelligence" hereby declare that the details furnished above are true and correct to the best of my knowledge and belief. In case any of the above information is found to be false or untrue or misleading or misrepresenting, I am aware that I may be held liable for it.