Abstract
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 The aim is 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 a 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 elderly 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.
- Received March 2, 2024.
- Revision received May 6, 2024.
- Accepted May 15, 2024.
- Copyright © 2024, The Authors
This article is Open Access: CC BY license (https://creativecommons.org/licenses/by/4.0/)