CT-Based Auto Lung Damage Assessment COVID-19

Abstract

Purpose: Monitoring disease development or viruses that invade our bodies, such as Coronavirus Disease of 2019 (COVID-19), can be effectively carried out using Computed Tomography (CT) imaging tools. However, manual assessment of CT images by consultants is often insufficient for determining the extent of lung damage in COVID-19 patients. Automated evaluation of lung damage addresses this limitation by optimizing healthcare resource utilization. It reduces the workload on radiologists, allowing them to concentrate on more complex cases. Additionally, it ensures accurate and consistent assessments of lung damage, minimizing variability and the potential for human error inherent in manual evaluations.

Materials and Methods: In this study, a new approach was presented for improving CT images of the lung and specifying further lesions. This will help calculate the extent of damage without human intervention. The structure of the proposed technique draws upon four phases (data collection, improvement, segmentation and extraction lung damage region and evaluation). Firstly, 100 patients were recruited between September 29 2020 and July 10, 2022, of whom tested positive for COVID-19 and CT images were collected, then composite technique is implemented to extract the percentage of lung damage of COVID-19 patients.

Results: The study results demonstrated an efficient method for quickly and practically calculating the percentage of lung damage. There is a clear convergence between manual evaluation, done by radiologists, and automatic evaluation using the proposed method, suggesting its potential as an alternative in the absence of a specialist doctor. The differences in the arithmetic mean between the proposed technique and the radiologists' evaluations were 3.5%, 10%, 18%, and 0.98% for radiologists 1, 2, 3, and 4, respectively. Additionally, the findings indicated that individuals aged 20-60 years are the most affected by COVID-19.

Conclusion: This method serves as a potent tool for swiftly and practically assessing the percentage of lung damage caused by COVID-19. By eliminating the need for human intervention, it enables the evaluation of lung damage autonomously. This feature makes it particularly valuable in telemedicine applications and emergency situations where specialist medical expertise may not be readily available.