Performance of artificial intelligence in the diagnosis of maxillary sinusitis in imaging examinations: Systematic review.

Abstract

Objectives: This systematic review aimed to assess the performance of artificial intelligence (AI) in the imaging diagnosis of maxillary sinusitis (MS) compared to human analysis.

Methods: Studies that presented radiographic images for the diagnosis of paranasal sinus diseases, as well as control groups for AI, were included. Articles that performed tests on animals, presented other conditions, surgical methods, didn't present data on the diagnosis of MS or on the outcomes of interest (area under the curve, sensitivity, specificity, and accuracy), compared the outcome only among different AIs, were excluded. Searches were conducted in five electronic databases and a gray literature. The risk of bias (RB) was assessed using the QUADAS-2 and the certainty of evidence by GRADE.

Results: Six studies were included. The type of study considered was retrospective observational; with serious RB, and a considerable heterogeneity in methodologies. The IA presents similar results to humans, however, imprecision was assessed as serious for the outcomes and the certainty of evidence was classified as very low according to the GRADE approach. Furthermore, a dose-response effect was determined, as specialists demonstrate greater mastery of the diagnosis of MS when compared to resident professionals or general clinicians.

Conclusions: Considering the outcomes, the AI represents a complementary tool for diagnosing MS, especially considering professionals with less experience. Finally, performance analysis and definition of comparison parameters should be encouraged considering future research perspectives.

Advances in knowledge: AI can be used as a complementary tool for diagnosing MS, however studies are still lacking methodological standardization.