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

Suri, Jasjit S.; Agarwal, Sushant; Gupta, Suneet Kr.; Puvvula, Anudeep; Biswas, Mainak; Saba, Luca; Bit, Arindam; Tandel, Gopal S.; Agarwal, Mohit; Patrick, Anubhav; Faa, Gavino; Singh, Inder; Oberleitner, Ron; Turk, Monika; Chadha, Paramjit S.; Johri, Amer M.; Sanches, J. Miguel; Khanna, Narendra N.; Višković, Klaudija; Mavrogeni, Sophie; Laird, John R.; Pareek, Gyan; Miner, Martin; Sobel, David; Balestrieri, Antonella; Sfikakis, Petros P.; Tsoulfas, George; Protogerou, Athanasios; Misra, Durga Prasanna; Agarwal, Vikas; Kitas, George D.; Ahluwalia, P. K.; Teji, Jagjit S.; Al-Maini, Mustafa; Dhanjil, Surinder; Sockalingam, Meyypan; Saxena, Ajit Kumar; Nicolaides, Andrew; Sharma, Aditya; Rathore, V. S.; Ajuluchukwu, J N; Fatemi, Mostafa; Alizad, Azra; Viswanathan, Vijay; Krishnan, Parvathy; Naidu, Subbaram

doi:10.1016/j.compbiomed.2021.104210

“…COVID-19 has unique ARDS characteristics in medical imaging and has been reported as a variable in several diagnostic studies. Artificial intelligence is a diagnostic tool that combines multiple 7 Disease Markers imaging modalities, including lung CT, chest radiography, and lung ultrasound [17]. Accordingly, AI assisted us to comprehensively interpret clinical and multiomics data of ARDS patients, and it is potentially advantageous in the management of ARDS patients in the future with individual treatment plans [18].…”

Section: Disease Markersmentioning

confidence: 99%

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study

Li

¹

,

Zhang

²

,

Qin

³

et al. 2021

View full text Add to dashboard Cite

Assessing the length of hospital stay (LOS) in patients with coronavirus disease 2019 (COVID-19) pneumonia is helpful in optimizing the use efficiency of hospital beds and medical resources and relieving medical resource shortages. This retrospective cohort study of 97 patients was conducted at Beijing You’An Hospital between January 21, 2020, and March 21, 2020. A multivariate Cox proportional hazards regression based on the smallest Akaike information criterion value was used to select demographic and clinical variables to construct a nomogram. Discrimination, area under the receiver operating characteristic curve (AUC), calibration, and Kaplan–Meier curves with the log-rank test were used to assess the nomogram model. The median LOS was 13 days (interquartile range [IQR]: 10–18). Age, alanine aminotransferase, pneumonia, platelet count, and PF ratio (PaO2/FiO2) were included in the final model. The C-index of the nomogram was 0.76 ( 95 % confidence interval CI = 0.69 – 0.83 ), and the AUC was 0.88 ( 95 % CI = 0.82 – 0.95 ). The adjusted C-index was 0.75 ( 95 % CI = 0.67 – 0.82 ) and adjusted AUC 0.86 ( 95 % CI = 0.73 – 0.95 ), both after 1000 bootstrap cross internal validations. A Brier score of 0.11 ( 95 % CI = 0.07 – 0.15 ) and adjusted Brier score of 0.130 ( 95 % CI = 0.07 – 0.20 ) for the calibration curve showed good agreement. The AUC values for the nomogram at LOS of 10, 20, and 30 days were 0.79 ( 95 % CI = 0.69 – 0.89 ), 0.89 ( 95 % CI = 0.83 – 0.96 ), and 0.96 ( 95 % CI = 0.92 – 1.00 ), respectively, and the high fit score of the nomogram model indicated a high probability of hospital stay. These results confirmed that the nomogram model accurately predicted the LOS of patients with COVID-19. We developed and validated a nomogram that incorporated five independent predictors of LOS. If validated in a future large cohort study, the model may help to optimize discharge strategies and, thus, shorten LOS in patients with COVID-19.

show abstract

“…Elsewhere, Tang et al [ 86 ] adopted a VB-net [ 7 ] to perform accurate segmentation of the whole lungs and lung lesions from CT images. Using U-Net with the initial seeds provided by a radiologist, Qi et al [ 37 ] presented segmentation of lesions in the lungs (see Table 2 ).…”

Section: Ai-based Workflows In the Assessment Of Images For Covid-19mentioning

confidence: 99%

“…For different types of pneumonia only one case was reported and therefore, the characteristics comparison between COVID-19 and other types of pneumonia failed [ 26 , 28 , 37 ]. Overall, for proper generalizability of models, multi-centric, large datasets are required.…”

Section: Limitations and Recommendations For Future Studiesmentioning

confidence: 99%

“…The authors summarized the COVID-19 prevalence in several parts of the world. Recently, Suri et al [ 37 ] investigated a variety of comorbidities and their associated risks in acute respiratory distress syndrome and mortality. They elaborated on AI architectures and their extension from pre-COVID-19 to post-COVID-19 and the views of seven school-of-though were summarised.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence-driven assessment of radiological images for COVID-19

Bouchareb

¹

,

Khaniabadi

²

,

Kindi

³

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

show abstract

“…This SARS-CoV-2 virus affects the respiratory system, damages lungs, travels through the entire body, and causes myocardial infarction or coronary artery syndrome [2,3] or worsening diabetes [4] or causing pulmonary embolism [5]. It was seen that comorbidity had a severe influence on COVID-19 [6]. As of today, even though vaccination is showing the signs of pandemic control in some parts of the world, it is still the highest concern for health professionals.…”

Section: Introductionmentioning

confidence: 99%

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Suri¹,

Agarwal²,

Pathak

³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

show abstract

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

Cited by 56 publications

References 206 publications

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study

Artificial intelligence-driven assessment of radiological images for COVID-19

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

Contact Info

Product

Resources

About