The Value of Artificial Intelligence in Post-processing Head and Neck CTA Images and Diagnosing Head and Neck Artery Stenosis

Objective: Discuss the value of Artificial Intelligence (AI) in post-processing head and carotid artery CTA images and diagnosing head and carotid artery stenosis. Methods: The imaging data of 42 patients who all underwent head and neck CTA and digital subtraction angiography (DSA) of head and carotid arteries from November 2022 to October 2023 at our hospital were retrospectively collected. Image post-processing and diagnosis were divided into AI and manual groups to compare the differences in image post-processing time and subjective scores of image quality and identification of head and carotid artery plaque properties (calcified plaque, non-calcified plaque and mixed plaque) between the two groups. To compare the differences in sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the two groups in diagnosing head and neck artery stenosis, using DSA results as the gold standard, and to perform the Kappa test for consistency between the two groups’ diagnostic results and DSA results. The diagnostic efficacy of the AI and manual groups on the head and carotid artery stenosis was analyzed by subject operator characteristic curve (ROC) and compared by Z-test. Results: The post-processing and diagnostic time in the AI group was (366.48±18.54) s, which was approximately 71.65% shorter than that in the manual group (1291.63±52.27) s. The difference was statistically significant. The difference between the subjective scores of image quality obtained by the two methods was not statistically significant. A total of 145 head and neck artery plaques were identified in the manual group and 145 in the AI group, of which 141 plaques of different nature were accurately identified in the AI group, with an overall accuracy rate of 97.24% (141/145). There was no statistically significant difference between the AI group and the manual group for identifying calcified plaque, non-calcified plaque and mixed plaque in the head and carotid arteries, and the agreement between the two groups for identifying the nature of plaque was good (Kappa=0.845). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the AI group in diagnosing head and neck artery stenosis were 87.09% (27/31), 81.82% (9/11), 93.10% (27/29), 69.23% (9/13) and 85.71% (36/42), respectively, which were in good agreement with DSA in diagnosing head and neck artery stenosis (Kappa= 0.792). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the manual group in diagnosing head and neck artery stenosis were 90.32% (28/31), 81.82% (9/11), 93.33% (28/30), 75.00% (9/12) and 88.10% (37/42), respectively, which were in good agreement with DSA in diagnosing head and neck artery stenosis (Kappa=0.801), and the difference in diagnostic efficacy was not statistically significant in either group. The results of the ROC curve analysis showed that the area under the curve (AUC) for the diagnosis of head and neck artery stenosis was 0.845 and 0.861 in the AI and manual groups, respectively, with no significant difference. Conclusion: AI technology has high clinical value in time spent on post-processing head and neck CTA images, assessing head and neck artery stenosis and identifying the nature of carotid plaque, and can be used as a useful aid for physicians in analyzing and diagnosing head and neck CTA.