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PURPOSE: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP). METHODS: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated. RESULTS: A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001). CONCLUSION: The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.
RESUMO
OBJECTIVE: The aim of this study was to investigate the effects of plaque-related factors on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). METHODS: Patients who underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) were retrospectively included in this study. The degree of stenosis in each vessel was collected from CCTA and ICA, and the information on plaque-related factors (plaque length, plaque type, and coronary artery calcium score (CAC)) of the vessels with plaques was collected from CCTA. RESULTS: In total, 1224 vessels in 306 patients (166 men; 65.7 ± 10.1 years) were analyzed. Of these, 391 vessels in 249 patients showed significant stenosis using ICA as the gold standard. Using per-vessel as the unit, the area under the curves of coronary stenosis ≥ 50% for AI-CADS, doctor, and AI-CADS + doctor was 0.764, 0.837, and 0.853, respectively. The accuracies in interpreting the degree of coronary stenosis were 56.0%, 68.1%, and 71.2%, respectively. Seven hundred fifty vessels showed plaques on CCTA; plaque type did not affect the interpretation results by AI-CADS (chi-square test: p = 0.0093; multiple logistic regression: p = 0.4937). However, the interpretation results for plaque length (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0061) and CACs (chi-square test: p < 0.0001; multiple logistic regression: p = 0.0001) were significantly different. CONCLUSION: AI-CADS has an ability to distinguish ≥ 50% coronary stenosis, but additional manual interpretation based on AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS. KEY POINTS: ⢠AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. ⢠Additional manual interpretation on the basis of AI-CADS is necessary. ⢠The plaque length and CACs will affect the diagnostic performance of AI-CADS.
