aceMultimodal Carotid Ultrasound-based Artificial Intelligence Model for Predicting Acute Ischemic Stroke Risk in Patients with Type 2 Diabetes Mellitus
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…
Combining deep learning, radiomic and clinical features from carotid ultrasound predicted acute ischemic stroke in type 2 diabetes patients with AUCs near 0.94-0.95, and AI assistance improved senior and junior readers' diagnostic performance.
Evidence
- Peer-reviewedAcademic Radiology2026-10-09
How should this claim be treated?
Truvace Impact Record TRV-2026-1651, v1: “Multimodal Carotid Ultrasound-based Artificial Intelligence Model for Predicting Acute Ischemic Stroke Risk in Patients with Type 2 Diabetes Mellitus.” Truvace, 2026-10-17. /record/TRV-2026-1651 (accessed at citation time). sha256 9d5bc1c6ecaf9863…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1651 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.