Abstract Number: Esoc2026a2179 Ultra-early Intracerebral Hemorrhage Detection Using Ai-assisted Transcranial Ultrasound: the Hyper-ai-scan Pilot Study
Year: 2026
Type: conference-abstract
Abstract: Background and aims Early differentiation between intracerebral hemorrhage (ICH) and ischemic stroke is critical, as management strategies differ substantially, yet this distinction is unavailable in prehospital and hyper-acute settings. This study assessed the feasibility and diagnostic performance of HYPER-AI-SCAN, an AI-assisted transcranial ultrasound framework for ICH detection. Methods In this single-centre prospective pilot study, 100 patients with acute stroke (33 ICH), confirmed by non-contrast CT, underwent portable transtemporal transcranial B-mode ultrasound using a strictly standardized 5-second sweep protocol applied by three operators with different neurosonology experience; infratentorial hemorrhages were excluded. Baseline characteristics were comparable between groups. Ultrasound videos were preprocessed and sampled using a first-pass policy (20 frames from first 2.5 s). A deep-learning pipeline combined a pre-trained ultrasound foundation model with attention-based multiple-instance learning for patient-level ICH classification. Ultrasound-only and cross-modal (NCCT-guided knowledge distillation) training strategies were evaluated, preserving ultrasound-only inference. Performance was assessed using 5-fold patient-disjoint cross-validation. Results In the ultrasound-only cohort (n = 100), balanced accuracy was 0.74 (95% CI 0.60–0.88), sensitivity 0.59 (0.39–0.79), specificity 0.87 (0.71–1.00), AUROC 0.85 (0.68–1.00), and F1 score 0.64 (0.47–0.81). In patients with paired ultrasound–CT data (n = 89), cross-modal training yielded balanced accuracy 0.80 (0.74–0.86), sensitivity 0.69 (0.58–0.80), specificity 0.91 (0.85–0.97), AUROC 0.89 (0.83–0.95), and F1 score 0.72 (0.63–0.81). Conclusions HYPER-AI-SCAN demonstrates the feasibility of AI-assisted, standardized transcranial ultrasound for reliable ICH detection in a real-world, multi-operator pilot setting, supporting scalable ultra-early and prehospital stroke triage. Conflict of interest nothing to disclose. Figure 1 - belongs to Methods
Source: European Stroke Journal
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,400
OpenAlex ID: W7160422216
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