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. 2022 Feb 17;22(1):28.
doi: 10.1186/s12880-022-00756-y.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience

Xianjun Han  1 , Nan Luo  1 , Lixue Xu  1 , Jiaxin Cao  1 , Ning Guo  2 , Yi He  1 , Min Hong  3 , Xibin Jia  4 , Zhenchang Wang  1 , Zhenghan Yang  5
Affiliations

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience

Xianjun Han et al. BMC Med Imaging. .

Abstract

Background: To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.

Methods: We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6.

Results: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2-+ 16.6% and + 5.9-+ 16.1%, respectively) and NPVs (range + 3.7-+ 13.4% and + 2.7-+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61).

Conclusions: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA. Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Keywords: Artificial intelligence (AI); CCTA; Coronary artery disease; Coronary stenosis; Inexperience readers.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of the patient selection process. CCTA coronary computed tomography angiography, ICA invasive coronary angiography
Fig. 2
Fig. 2
Single-centre retrospective case of a noncalcified plaque lesion in the proximal LAD that caused severe stenosis (white arrow). a CPR image reconstructed manually, b ICA image of the left coronary artery, c CPR, VRT and straightened images automatically reconstructed by the AI system. ICA invasive coronary angiography, LAD left anterior descending, LCx left circumflex
Fig. 3
Fig. 3
AUC comparison of the six readers at the patient level
Fig. 4
Fig. 4
AUC comparison of the six readers at the vessel level
See this image and copyright information in PMC

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