Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives

Table 1 Summary of artificial intelligence-based carotid computed tomography angiography plaque detection studies

Ref.	Sample size	Model type	Dataset source	Results	Clinical impact
Jie et al[6]	3245 (meta-analysis, 17 studies)	Mixed AI models	Multicenter/multi-country	Sensitivity of 0.91, specificity of 0.88, AUC of 0.94	Improved CTA plaque detection accuracy
Pisu et al[7]	156	ML	Single-center CTA	AUC of 0.91, sensitivity of 87%, specificity of 85%	Early identification of high-risk symptomatic plaques
Shi et al[8]	112	Radiomics + logistic regression	Single-center CTA	AUC of 0.89	Differentiates plaque stability, optimizes treatment planning
Zhai et al[9]	1234	Convolutional neural network (fully automatic detection)	Multicenter CTA	Sensitivity of 0.93, specificity of 0.92	Rapid automated plaque screening
Hu et al[10]	205	Radiomics + dual-energy CTA	Multicenter CTA	AUC of 0.90, accuracy of 88%	Improved symptomatic plaque recognition
Guo et al[11]	120	Two-stage deep learning	Single-center CTA	Sensitivity of 0.92, specificity of 0.89	Good performance in early-stage validation
Xie et al[12]	560	Swin-UNet + multi-scale supervision	Multicenter CTA	Dice coefficients of 0.93	High-precision segmentation for quantitative analysis
Song et al[13]	98	AI segmentation + ultrasound radiomics	Single-center ultrasound	AUC of 0.88	Ultrasound-based method applicable to CTA risk assessment
Wei et al[14]	4562	ML (random forest, etc.)	Single-center health check-up	AUC of 0.85	Early population risk prediction tool

AI: Artificial intelligence; AUC: Area under the curve; CTA: Computed tomography angiography; ML: Machine learning.