Background and aims: Artificial intelligence (AI) refers to the use of computational techniquesto mimic human thought processes and learning capacity.
AI in radiology is developing day byday, and as an emerging frontier of technology, it is rapidly becoming recognized in the field ofmedicine, including
computer tomography (CT). This study aims to investigate the use and roleof
AI in CT.Method: This review study was conducted on February ۱۰, ۲۰۲۳, by searching the reliable databasesof PubMed, Scopus, and Web of Science. English language articles from ۲۰۲۱ to ۲۰۲۳ thatwere in line with our goal were included in this study. Exclusion criteria included articles withabstracts without text, letters to the editor, and a lack of access to the full text of the articles. Eligiblecriteria were independently screened by the authors. The same checklist was used to extractdata such as references, year of publication, name of the country, and important related points.Results: Finally, ۲۲ related articles were included in this review. The highest number of articleswas conducted in ۲۰۲۲, and also, most studies were related to the countries of America, China,Iran, Denmark, and Germany.
AI was mostly used in
CT during the COVID-۱۹ pandemic for lungimaging (۶/۲۳), and lung imaging for renal disease (۴/۲۳), lung cancer (۳/۲۳), heart and coronaryarteries (۴/۲۳) ), chest (۱/۲۳), bone metastases (۱/۲۳), sacral bone (۱/۲۳), colitis and Crohn’s disease(۱/۲۳) and acute bleeding (۱/۲۳). All studies were conducted for two reasons: diagnosis andevaluation of the disease or prognosis and prediction, which was for diagnosis and evaluation ofthe disease (۱۳ studies), and for better prognosis and prediction of the disease (۹ studies).
CT imagingusing
AI algorithms (۷ studies), deep learning methods (۵ studies), neural network models(۵ studies), computer-aided design (CAD) (۱ study), ۳D model (۱ study), and radionics (۱ study).Conclusion:
AI methods for disease diagnosis and prediction in
CT play an important role indisease monitoring and management. It has greatly increased the efficiency of diagnosis and has agreater role in the accurate assessment and analysis of disease reports and prognoses.