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Updated: Jun 24, 2025
Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis
Published on: February 10, 2023
Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard
Kerstin Nothnagel1, Mohammed Farid Aslam2
1Population Health Sciences, Canynge Hall, Bristol Medical School, University of Bristol, Bristol, UK Kerstin.Nothnagel@bristol.ac.uk.
Background:
This study evaluates the feasibility of remote deep venous thrombosis (DVT) diagnosis via ultrasound sequences facilitated by ThinkSono Guidance, an artificial intelligence (AI) app for point-of-care ultrasound (POCUS).
Aim:
To assess the effectiveness of AI-guided POCUS conducted by non-specialists in capturing valid ultrasound images for remote diagnosis of DVT.
Design & Setting:
Over a 3.5-month period, patients with suspected DVT underwent AI-guided POCUS conducted by non-specialists using a handheld ultrasound probe connected to the app. These ultrasound sequences were uploaded to a cloud dashboard for remote specialist review. Additionally, participants received formal DVT scans.
Method:
Patients underwent AI-guided POCUS using handheld probes connected to the AI app, followed by formal DVT scans. Ultrasound sequences acquired during the AI-guided scan were uploaded to a cloud dashboard for remote specialist review, where image quality was assessed, and diagnoses were provided.
Results:
Among 91 predominantly older female participants, 18% of scans were incomplete. Of the rest, 91% had sufficient quality, with 64% categorised by remote clinicians as 'compressible' or 'incompressible'. Sensitivity and specificity for adequately imaged scans were 100% and 91%, respectively. Notably, 53% were low risk, potentially obviating formal scans.
Conclusion:
ThinkSono Guidance effectively directed non-specialists, streamlining DVT diagnosis and treatment. It may reduce the need for formal scans, particularly with negative findings, and extend diagnostic capabilities to primary care. The study highlights AI-assisted POCUS potential in improving DVT assessment.
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