Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study.

Purpose: The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA).

Materials and methods: Patients who underwent head and neck CTA examinations at two hospitals were retrospectively included. CTA examinations were randomized into group 1 (without AI-washout-with AI) and group 2 (with AI-washout-without AI), and six readers (two radiology residents, two non-neuroradiologists, and two neuroradiologists) independently interpreted each CTA examination without and with AI solutions. Additionally, reading time was recorded for each patient. Digital subtraction angiography was used as the standard of reference. The diagnostic performance for AS at lesion and patient levels with four AS thresholds (30 %, 50 %, 70 %, and 100 %) was assessed by calculating sensitivity, false-positive lesions index (FPLI), specificity, and accuracy.

Results: A total of 268 patients (169 men, 63.1 %) with a median age of 65 years (first quartile, 57; third quartile, 72; age range: 28-88 years) were included. At the lesion level, AI improved the sensitivity of all readers by 5.2 % for detecting AS ≥ 30 % (P < 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all P < 0.05). At the patient level, AI improved the accuracy of all readers by 4.1 % (73.9 % [1189/1608] without AI vs. 78.0 % [1254/1608] with AI) (P < 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (P = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (P< 0.001).

Conclusion: AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.