Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.

Abstract

Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was conducted from November 2022 to March 2023. A sham-AI model with near-zero sensitivity and similar specificity to a standard AI model was developed using 16 422 CT angiography examinations. Digital subtraction angiography-verified CT angiographic examinations from four hospitals were collected, half of which were processed by standard AI and the others by sham AI to generate sequence A; sequence B was generated in the reverse order. Twenty-eight radiologists from seven hospitals were randomly assigned to either sequence and then assigned to the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with standard-AI assistance, and radiologists with sham-AI assistance were compared using sensitivity and specificity, and radiologists' susceptibility to sham AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61.0 years [IQR, 52.0-67.0]; 199 male), 50 of whom had aneurysms. Standard AI and sham AI performed as expected (sensitivity, 96.0% vs 0.0%; specificity, 82.0% vs 76.0%). The differences in sensitivity and specificity between standard AI-assisted and sham AI-assisted readings were 20.7% (95% CI: 15.8, 25.5 [superiority]) and 0.0% (95% CI: -2.0, 2.0 [noninferiority]), respectively. The difference between sham AI-assisted readings and radiologists alone was -2.6% (95% CI: -3.8, -1.4 [noninferiority]) for both sensitivity and specificity. After sham-AI suggestions, 5.3% (44 of 823) of true-positive and 1.2% (seven of 577) of false-negative results of radiologists alone were changed. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed sham-AI model compared with their unassisted performance. Keywords: CT Angiography, Vascular, Intracranial Aneurysm, Sham AI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Mayfield and Romero in this issue.