Imaging and COVID-19: Preparing the radiologist for the pandemic

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Background and objective

In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.

Methods

The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.

Results

The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.

Conclusion

This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language

The diagnosis of coronavirus disease 2019 (COVID-19) is confirmed by reverse transcription polymerase chain reaction. The utility of chest radiography (CXR) remains an evolving topic of discussion. Current reports of CXR findings related to COVID-19 contain varied terminology as well as various assessments of its sensitivity and specificity. This can lead to a misunderstanding of CXR reports and makes comparison between examinations and research studies challenging. With this need for consistency, we propose language for standardized CXR reporting and severity assessment of persons under investigation for having COVID-19, patients with a confirmed diagnosis of COVID-19, and patients who may have radiographic findings typical or suggestive of COVID-19 when the diagnosis is not suspected clinically. We recommend contacting the referring providers to discuss the likelihood of viral infection when typical or indeterminate features of COVID-19 pneumonia on CXR are present as an incidental finding. In addition, we summarize the currently available literature related to the use of CXR for COVID-19 and discuss the evolving techniques of obtaining CXR in COVID-19-positive patients. The recently published expert consensus statement on reporting chest computed tomography findings related to COVID-19, endorsed by the Radiological Society of North American (RSNA), the Society of Thoracic Radiology (STR), and American College of Radiology (ACR), serves as the framework for our proposal.