The Role of AI in the COVID-19 Pandemic

Covid-19 started its havoc in January 2020, It has tainted millions of individuals overall. It causes
intense respiratory issues in patients leading to a high mortality rate. Lab affirmation of SARS-CoV-2 is performed with an infection explicit RT-PCR, however, it has a few uncertainties, including high bogus negative rates, delays in handling, and changes in test strategies. A CT scan shows the attributes of each phase of infection and its advancement. Besides the numerous challenges that actually exist in the methods of COVID-19 detection, there are a few medical attributes that can be still relied on. As of now, the doctors frequently get the CT images and patient information from the medical system. These CT scan images are analyzed through Picture Archiving System and the derived results are sent back to the Medical System. The Picture Arching System operates on a FIFO protocol analyzing the CT images in the order it is received. There is no parameter of the severity of infection involved for the analysis. This leads to a high mortality rate due to the delay in the COVID-19 test results and its action of treatment.

AI research teams have come up with plenty of methods and models that can automatically provide the probability of infection and the ranked patient order. Methods involved:

1. Data Collection:
   The very first step of building an AI-assisted diagnosis system for COVID-19 is Data Collection, inwhich Chest X-ray and CT images are most widely used. More applications are using CT images for COVID-19 diagnosis since the analysis and segmentation of CT images are always more precise and efficient than X-ray images. Some efforts have been made on contactless data collection to reduce the risk of infection during COVID-19. A mobile CT platform is built which has more flexible access to patients. During CT data collection, the positioning and scanning of patients are operated remotely by a technician.
   
2. CT Segmentation:
   The CT image segmentation and deep neural network play an important role in AI-assisted COVID-19 analysis. It highlights the regions of interest in CT images for further examination. The segmentation tasks in COVID-19 applications can be divided into two groups: Lung Region segmentation and Lung Lesion segmentation. During Lung Region segmentation, the whole lung region is separated from the background, while in Lung Lesion segmentation tasks the abnormal or infected areas are distinguished from other lung areas. A V-Net-based segmentation model which is a CNN model is preferred to separate lung lesions and lung regions and extract the radiologic characteristics to predict the hospital stay of a patient. A V-Net-based segmentation model which is a CNN model is preferred to separate lung lesions and lung regions and extract the radiologic characteristics to predict the hospital stay of a patient. A V-Net-based segmentation model which is a CNN model is preferred to separate lung lesions and lung regions and extract the radiologic characteristics to predict the hospital stay of a patient. A V-Net-based segmentation model which is a CNN model is preferred to separate lung lesions and lung regions and extract the radiologic characteristics to predict the hospital stay of a patient.
   
   Conclusion:
   This blog provides a basic overview on one of the methods of COVID-19 detection, i.e. CT Segmentation. However, there are a number of other effective AI models and methods in practice and under implementation.