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

Abstract

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.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

“…While the focus of this study is on AI severity scoring, multivariate modelling of AI segmented measurements has an advantage over a scoring heuristic for the diagnosis of COVID-19 pneumonia. [ 13 , 14 ] A multivariate model consisting of opacity volume, “high opacity” volume, and the standard deviation of both opacity Hounsfield units and total Hounsfield units provides an AUC of 0.805, greater than the sum of its parts or the opacity scoring system. Expert measurement of opacity volumes and standard deviations are not feasible, reflecting a possible advantage of using AI systems in the prediction of COVID-19 pneumonia.…”

Lessons from SARS‑CoV‑2 and its variants (Review)

“…ai applications are used in imaging platforms, region segmentation for lung infection, clinical assessment and auxiliary diagnosis based on meta-analysis (74). Based on its operational principles of interpreting chest imaging and quick diagnosis (77), it may also contribute to clinical and basic research associated with SarS-coV-2, in addition to assisting diagnosis in clinical practice. accurately distinguishing coVid-19 from other respiratory disease improves the efficiency of diagnoses and simplifies workflow, which primarily depends on manual work of radiologists, providing more accurate results and maintaining safety of medical staff during examination (75).…”

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

“…Medical images could be converted into high-dimensional data through radiomics, wherein radiomics features are selected from the images and combined using machine learning algorithms to arrive at radiomics signatures as biomarkers of disease. In addition to wide usage in several oncologic [ [24] , [25] , [26] ] as well as non-oncologic diseases [ [27] , [28] , [29] ], radiomics studies have indicated that imaging features extracted from CT or chest X-ray images could be used as parameters for outcome prediction of patients with COVID-19 pneumonia. Radiomics analyses have been applied to different aspects of COVID-19, including diagnosis, severity scoring, prognosis, hospital/ICU stay prediction, and survival analysis [ [20] , [21] , [22] ].…”