X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic

Purpose. Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases. Method. This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity. Result. The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19. Conclusion. X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.

Automated summary

This study systematically reviewed the application of artificial intelligence in X-ray imaging analysis for diagnosing COVID-19 cases. It found that X-rays equipped with AI algorithms had an average sensitivity and specificity of over 96% and 92% respectively, significantly higher than common X-ray methods for COVID-19 detection.