AI Insight
This multicenter study evaluated an AI guidance system that enables non-specialists to perform ultrasound scans for detecting deep vein thrombosis (DVT) in the legs. Among 594 patients, the AI-guided scans achieved diagnostic quality in 87% of cases, detected DVT with 93% sensitivity, and successfully identified 98% of patients who needed standard ultrasound follow-up. The system completed scans and review in a median time of 7.57 minutes and could potentially eliminate the need for standard ultrasound in 35% of patients.
Why it matters
This technology could significantly expand access to DVT diagnosis in settings where ultrasound expertise is limited, such as rural areas, emergency departments during off-hours, or resource-constrained healthcare systems. By enabling rapid triage and reducing unnecessary specialist ultrasounds, it addresses critical delays in diagnosing a potentially life-threatening condition.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings
Source: Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation