Machine learning for medical imaging‐based COVID‐19 detection and diagnosis

Rehouma, Rokaya; Buchert, Michael; Chen, Yi‐Ping Phoebe

The novel coronavirus disease 2019 (COVID‐19) is considered to be a significant health challenge worldwide because of its rapid human‐to‐human transmission, leading to a rise in the number of infected people and deaths. The detection of COVID‐19 at the earliest stage is therefore of paramount importance for controlling the pandemic spread and reducing the mortality rate. The real‐time reverse transcription‐polymerase chain reaction, the primary method of diagnosis for coronavirus infection, has a relatively high false negative rate while detecting early stage disease. Meanwhile, the manifestations of COVID‐19, as seen through medical imaging methods such as computed tomography (CT), radiograph (X‐ray), and ultrasound imaging, show individual characteristics that differ from those of healthy cases or other types of pneumonia. Machine learning (ML) applications for COVID‐19 diagnosis, detection, and the assessment of disease severity based on medical imaging have gained considerable attention. Herein, we review the recent progress of ML in COVID‐19 detection with a particular focus on ML models using CT and X‐ray images published in high‐ranking journals, including a discussion of the predominant features of medical imaging in patients with COVID‐19. Deep Learning algorithms, particularly convolutional neural networks, have been utilized widely for image segmentation and classification to identify patients with COVID‐19 and many ML modules have achieved remarkable predictive results using datasets with limited sample sizes.