Diagnostic Accuracy of Artificial Intelligence Models in Computed Tomography Interpretation of Chronic...

Chronic rhinosinusitis (CRS) is a prevalent condition frequently evaluated using computed tomography (CT). The application of artificial intelligence (AI) in CRS imaging analysis is expanding; however, comprehensive assessments of its diagnostic performance remain scarce.

To systematically review and compare the diagnostic accuracy of AI models for interpreting CT images of CRS, focusing on sensitivity, specificity, accuracy, and area under the curve (AUC).

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six databases were searched for original studies that assessed AI, machine learning, or deep learning models for CRS diagnosis using CT. Eligible studies reported diagnostic performance metrics for human subjects. The extracted data included the AI model type, validation method, imaging modality, reference standard, and diagnostic outcomes.

Of the 1502 screened articles, 6 studies involving 2178 patients met the inclusion criteria. Most utilized convolutional neural networks, residual networks (ResNets), or hybrid deep learning models. The sensitivity, specificity, and accuracy ranged from 11.1% to 98.1%, 86.4% to 98.7%, and 63.6% to 98.4%, respectively. AUC values could reach 0.99.

AI, particularly ResNets-based models, demonstrates promising diagnostic accuracy for CRS CT interpretation. However, methodological heterogeneity limits comparability, underscoring the need for standardized, multicenter validation and integration of clinical data to enhance generalizability.