Related Experiment Video
Updated: Oct 25, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review
Hanqiu Deng1,2, Xingyu Li1
1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
Insights
Artificial intelligence (AI) models trained on lung scans offer rapid screening for COVID-19. This review covers AI methods for detecting coronavirus disease, segmenting infections, and predicting patient prognosis using chest imaging.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- Coronavirus disease 2019 (COVID-19) spread globally, necessitating rapid diagnostic tools beyond PCR tests, which can yield false negatives.
- Chest X-rays and CT scans provide crucial data for evaluating suspected COVID-19 cases.
- The limitations of traditional diagnostic methods highlight the need for advanced screening solutions.
Purpose of the Study:
- To provide a comprehensive review of state-of-the-art artificial intelligence (AI)-empowered methods for the computational examination of COVID-19 patients using lung scans.
- To categorize and analyze AI-driven approaches for COVID-19 detection, infection segmentation, and severity assessment.
- To summarize publicly available lung scan datasets and discuss future research directions.
Main Methods:
- A systematic literature search was conducted on bioRxiv, medRxiv, and arXiv for papers and preprints published between January 1, 2020, and March 31, 2021.
- Keywords used included "COVID", "lung scans", and "AI".
- 96 studies were included after quality screening and categorized by application: detection, segmentation, and severity/prognosis.
Main Results:
- AI models trained on lung scans demonstrate potential as quick diagnostic and screening tools for COVID-19.
- Reviewed studies showcase AI's capability in automatic detection, infection segmentation, and severity assessment from chest images.
- The review presents advantages and limitations of various AI solutions for COVID-19 analysis.
Conclusions:
- AI-powered analysis of lung scans is a promising approach to enhance COVID-19 screening efficiency and accessibility.
- The availability of public lung scan datasets aids in the development and validation of AI models.
- Addressing current research challenges is crucial for designing effective computational solutions against future pandemics.
Abstract:
Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.
More Related Videos
Related Concept Videos
01:13Radiological Investigation III: Pulmonary Angiogram and PET Scan
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
01:29Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care
01:28Imaging Studies for Cardiovascular System V: CT
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
01:30Radiological Investigation I: X-ray and CT
01:30Radiological Investigation II: MRI and Ventilation Perfusion Scan
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...