Academic radiology · 2026
Multimodal Carotid Ultrasound-based Artificial Intelligence Model for Predicting Acute Ischemic Stroke Risk in Patients with Type 2 Diabetes Mellitus.
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Sun Yaning, Feng Bojian, Zhang Yanming, Ou Di, Pan Dangin, Wang Saisai, Shen Fengqin, Li Xinchen, Xu Dong
Аннотация
RATIONALE AND OBJECTIVES: Patients with type 2 diabetes mellitus (T2DM) are at high risk of acute ischemic stroke (AIS), yet accurate risk stratification remains challenging. This study aimed to develop and validate a multimodal model based on carotid ultrasound for improved AIS risk prediction.
MATERIALS AND METHODS: In this multicenter retrospective study, a total of 480 patients with T2DM who underwent carotid ultrasound were recruited from two centers, with one center (n=394) used for model development and the other (n = 86) for independent external testing. A dual-scale deep learning framework based on a Swin Transformer was developed to capture both local plaque features and surrounding vascular context. Deep learning features were integrated with radiomic and clinical features to construct a multimodal model. Model performance was evaluated using the area under the curve (AUC), along with calibration and decision curve analysis.
RESULTS: The multimodal model achieved AUCs of 0.952 (95% CI: 0.910-0.984) in the internal cohort and 0.939 (95% CI: 0.838-0.996) in the external cohort, showing improved performance compared to single-modality models. In the reader study, AI-assisted interpretation improved diagnostic performance across all readers, with AUC increases ranging from 0.760 to 0.857 and from 0.868 to 0.929 for senior readers, and from 0.450 to 0.700 and from 0.548 to 0.659 for junior readers. Generalized estimating equation analyses showed significant improvements in diagnostic correctness among both senior and junior readers, with P values of 0.00235 and 0.000343, respectively.
CONCLUSION: A multimodal framework integrating carotid ultrasound with clinical, radiomic, and deep learning features enables improved AIS risk stratification in patients with T2DM.
MATERIALS AND METHODS: In this multicenter retrospective study, a total of 480 patients with T2DM who underwent carotid ultrasound were recruited from two centers, with one center (n=394) used for model development and the other (n = 86) for independent external testing. A dual-scale deep learning framework based on a Swin Transformer was developed to capture both local plaque features and surrounding vascular context. Deep learning features were integrated with radiomic and clinical features to construct a multimodal model. Model performance was evaluated using the area under the curve (AUC), along with calibration and decision curve analysis.
RESULTS: The multimodal model achieved AUCs of 0.952 (95% CI: 0.910-0.984) in the internal cohort and 0.939 (95% CI: 0.838-0.996) in the external cohort, showing improved performance compared to single-modality models. In the reader study, AI-assisted interpretation improved diagnostic performance across all readers, with AUC increases ranging from 0.760 to 0.857 and from 0.868 to 0.929 for senior readers, and from 0.450 to 0.700 and from 0.548 to 0.659 for junior readers. Generalized estimating equation analyses showed significant improvements in diagnostic correctness among both senior and junior readers, with P values of 0.00235 and 0.000343, respectively.
CONCLUSION: A multimodal framework integrating carotid ultrasound with clinical, radiomic, and deep learning features enables improved AIS risk stratification in patients with T2DM.