  Purpose: Artificial intelligence (AI) techniques have been extensively
utilized for diagnosing and prognosis of several diseases in recent years. This
study identifies, appraises and synthesizes published studies on the use of AI
for the prognosis of COVID-19. Method: Electronic search was performed using
Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that
examined machine learning or deep learning methods to determine the prognosis
of COVID-19 using CT or chest X-ray images were included. Polled sensitivity,
specificity area under the curve and diagnostic odds ratio were calculated.
Result: A total of 36 articles were included; various prognosis-related issues,
including disease severity, mechanical ventilation or admission to the
intensive care unit and mortality, were investigated. Several AI models and
architectures were employed, such as the Siamense model, support vector
machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural
networks. The models achieved 71%, 88% and 67% sensitivity for mortality,
severity assessment and need for ventilation, respectively. The specificity of
69%, 89% and 89% were reported for the aforementioned variables. Conclusion:
Based on the included articles, machine learning and deep learning methods used
for the prognosis of COVID-19 patients using radiomic features from CT or CXR
images can help clinicians manage patients and allocate resources more
effectively. These studies also demonstrate that combining patient demographic,
clinical data, laboratory tests and radiomic features improves model
performances.
