Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators
File(s)
Published version
Author(s)
Speranza, Giancarlo
Mischkewitz, Sven
Al-Noor, Fouad
Kainz, Bernhard
Type
Journal Article
Abstract
Deep vein thrombosis (DVT) carries high morbidity, mortality, and costs globally. Point of care ultrasound (POCUS) image acquisition by non-ultrasound-trained providers, supported by an AI-based guidance and remote image review system, is believed to improve the timeliness and cost-effectiveness of diagnosis. We examine a database of 381 patients with suspected DVT who underwent an AI-guided ultrasound scan by a non-ultrasound-trained nurse and an expert sonographer-performed standard compression ultrasound scan. Each AI-guided scan was reviewed remotely by blinded radiologists or blinded independent POCUS-certified American Emergency Medicine (EM) physicians. Remote reviewer and standard scan diagnoses were compared. The primary endpoint is AI-guidance system sensitivity with clinician review; secondary endpoints include specificity, positive predictive value, negative predictive value, image quality, inter-observer image quality, and vein compressibility agreement. Data was analysed through the bootstrapping method, bootstrapping with a second reader for each scan, and a majority voting system. Eighty percent (n = 304) of scans were of sufficient diagnostic quality. Radiologist reviewer sensitivity ranged from 90%–95%, specificity from 74%–84%, NPV from 98%–99%, PPV from 30%–42%, and potential expert-led ultrasound scans avoided from 39%–50%. Inter-observer agreement for image quality was 0.15 and for compressibility 0.61. EM reviewer sensitivity ranged from 95%–98%, specificity from 97%–100%, NPV was 99%, PPV from 81%–100%, and potential expert-led ultrasound scans avoided from 29%–38%. Inter-observer agreement for image quality was 0.59 and for compressibility 0.67. Diagnosing lower extremity DVT through AI-guided image acquisition with clinician review is feasible. Performance is influenced by reviewer expertise. We find potential positive impacts on health economics, including safely avoiding expert-led ultrasound scans.
Date Issued
2025-03-01
Date Acceptance
2025-02-16
Citation
npj Digital Medicine, 8
ISSN
2398-6352
Publisher
Springer Science and Business Media LLC
Journal / Book Title
npj Digital Medicine
Volume
8
Issue
1
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40025255
PII: 10.1038/s41746-025-01518-0
Publication Status
Published
Coverage Spatial
England
Article Number
135
Date Publish Online
2025-03-01