Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .
It was breaking news that the infected cases can be confirmed via tests , called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, there are many areas without the accessibility of such RT-PCR tests. Besides, this laboratory test also has high false-negative rates due to difficulty in quality control during sample preparation. All those led to a worse situation that the quick spread of pandemic around the world hit the record. Thus, appropriate rapid and accurate image processing tools, especially for X-ray and CT-based imaging tools, are of great help to the physician. For instance, in Italy, the United States, and China, the majority of COVID-19 cases have been identified through the manifestation characteristics in the computed tomography (CT) images . Otherwise, suspicious patients, even without common COVID-19 symptoms such as fever, cough, and shortness of breath, were hospitalized or quarantined until the outcome of laboratory tests were clear . Moreover, due to the high false positives rate of RT-PCR test, many suspect patients had to be tested several times for confirmed positive or negative results. Therefore, the acquisition of images such as X-ray and CT scans chest images play an important role in limiting viral transmission and the appropriate stages of treatment in the fight against the COVID-19.
Scientists have made significant contributions to the campaign of fighting against COVID-19, there is growing research findings, with each passing day, such as research reports and publications reported by both industrial and academic researchers.In the first 80 days of 2020, the number of published articles has increased from 775 to 1245 on contagious viruses. Similarly, significant research activities were booming after the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic and the Middle East Respiratory Syndrome (MERS) epidemic in 2012. Both SARS and MERS are caused by a coronavirus.
The introduction of Artificial Intelligence (AI) to the field of medical imaging research has been seen a promising role in the diagnosis and prediction of the disease. Compared to traditional image processing techniques, AI-based image analysis techniques provide more accurate, efficient, speedy, reliable and reproducible information about the diseases. AI-based techniques for the diagnosis of COVID-19 are based on image analysis, image segmentation of infected lung regions, and classifications for clinical evaluation. These AI-based techniques have shown great potential to be commercialized.
AI in Diagnostic of COVID-19
During this ongoing pandemic, patients suspected of COVID- 19 must seek for an urgent diagnosis and prompt treatment for early containment. Manual analysis of the medical images obtained by radiologists is a time-consuming procedure and is prone to some human error. Moreover, COVID-19 is a new deadly, and challenging disease that has symptoms almost similar to those of other infectious diseases such as Severe Acute Respiratory Syndrome (SARS). More domain knowledge is needed from expert and experienced radiologists for the precise diagnosis of COVID-19. Therefore, well-trained AI models can relieve human labor by learning radiologists’ knowledge and building in the computational models to ensure accurate and fast diagnosis. Generally, AI-based methods for diagnosis of COVID-19 consists of image segmentation and classification tasks on the CT or X-ray chest images.
CT-image segmentation for diagnostic of COVID-19
The CT images are utilized for the diagnosis of COVID-19, and normal CT images are called CT-non-COVID-19 images, and infected CT images are called CT-COVID-19 images as shown.
COVID-19 and this is one of the reasons why they classify the database into severe and non-severe cases. Mild and common cases were considered to be non-severe and as serious and critical were combined as serious. But sometimes mild cases lead to serious stages as well.
Overall analysis of AI-based methods for COVID-19 detection
The study of AI-based COVID-19 detection methods is important for potential automatic and precise clinical diagnosis and timely and appropriate treatment.
Given the quick outbreak of COVID-19, many methods are based on machine learning or deep learning models with limited ground truth databases. on the detection of COVID-19 from chest CT images or X-ray images, as summarised
AI has played an important role in the analysis and the diagnosis based on medical imaging, especially the detection of brain tumors, and eye diseases. At present, AI has played a vital role in the implementation of the COVID-19 imaging-based diagnostic system. Various image sources have been used to diagnose COVID-19 such as CT scan images and CXR images. Over a short period of six months, AI researchers have come up with many effective methods to diagnose COVID-19 images, but there is still room for development in the future. AI has proven to be a powerful tool for image acquisition to make scanning procedures efficient and safer by protecting medical personnel from COVID-19. The main objective of empowering AI for the diagnosis of COVID-19 from CXR and CT images is to facilitate better quality scanning and reduce radiation exposure to patients. It is very crucial to use the appropriate amount of radiation during the scan and to keep a minimum dose of imaging.
