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Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators

Value of clinical review for AI-guided deep vein thrombosis diagnosis

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.

Remote Expert DVT Triaging

Abstract: Our study shows that an AI-guided compression ultrasound with remote expert review for DVT can be a safe and effective method to reduce the number of necessary formal ultrasound scans. In this way, the diagnosis may be made in a more time-efficient and cost-sensitive manner, possibly leading to better health and quality outcomes for patients.

Machine-learning software aids non-experts in performing “safe and efficient” remote DVT triage

Abstract: The machine-learning software was able to aid non-experts in acquiring valid ultrasound images of venous compressions and allowed safe and efficient remote triaging. Given that the vast majority of the requested DVT scans are negative, such a triaging strategy allows faster diagnosis and treatment of high-risk patients and can spare the need and cost of multiple unnecessary duplex scans. Patient waiting times can be reduced, and radiologist and sonographer resources can be reallocated.

AI-guided novice-user compression sonography with remote expert DVT diagnosis

Abstract: AutoDVT (ThinkSono GmbH, Potsdam, Germany), a novel machine-learning software, provides a tool to aid non-specialists in acquiring appropriate compression sequences for remote DVT assessment. Ultrasound clips can then be reviewed by an expert remotely to triage suspected DVT patients better, as well as potentially diagnose them. This could result in decreased costs due to better and earlier triaging and diagnosis of patients.

AutoDVT: Joint Real-time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics.

Abstract: Abstract: We propose a dual-task convolutional neural network (CNN) to fully automate the real-time diagnosis of deep vein thrombosis (DVT). DVT can be reliably diagnosed through evaluation of vascular compressibility at anatomically defined landmarks in streams of ultrasound (US) images.

Remote Expert Deep Venous Thrombosis Triaging of Novice-User Compression Sonography with Artificial Intelligence Guidance

Abstract: Machine learning software was able to aid nonexperts in acquiring valid ultrasound images of venous compressions and allowed safe and efficient remote triaging. Such a triaging strategy allows faster diagnosis and treatment of high-risk patients and can spare the need and cost of multiple unnecessary duplex scans.

Evaluating the benefits of machine learn

Abstract: 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).

ADVENT: A Multi-Site Study to Validate The Efficacy of ThinkSono AI-Guidance

Ultrasound is one of the most widely requested forms of diagnostic imaging. The costs for diagnosing deep vein thrombosis (DVT) in the UK are £175 million, annually. In at least 80% of cases, DVT is excluded. As health care provision becomes increasingly stretched, resource utilization needs to be optimized. This prospective, double-blind, test accuracy study was designed to test whether an artificial intelligence (AI)–guided software device (AutoDVT) could support non-radiology specialists to diagnose proximal DVT.

Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning.

Abstract: Abstract: We train a deep learning algorithm on ultrasound videos from 246 healthy volunteers and evaluate on a sample size of 51 prospectively enrolled patients from an NHS DVT diagnostic clinic. 32 DVT-positive patients and 19 DVT-negative patients were included. Algorithmic DVT diagnosis results …

Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators

Value of clinical review for AI-guided deep vein thrombosis diagnosis

Journal of Vascular Surgery: Venous and Lymphatic Disorders

Remote Expert Deep Venous Thrombosis Triaging of Novice-User Compression Sonography with Artificial Intelligence Guidance

Annals of Vascular Surgery

Remote Expert DVT Triaging

A feasability study

Evaluating the benefits of machine learn

Venous News

Machine-learning software aids non-experts in performing “safe and efficient” remote DVT triage

ADVENT: A Multi-Site Study to Validate The Efficacy of ThinkSono AI-Guidance for Use By Non-Specialist Practitioners in the UK

ADVENT: A Multi-Site Study to Validate The Efficacy of ThinkSono AI-Guidance

ECR2022

AI-guided novice-user compression sonography with remote expert DVT diagnosis

NATURE DIGITAL MEDICINE (NPJ) – 2021

Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTIONS – 2018

AutoDVT: Joint Real-time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics.

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Regulatory Approval & Compliance

ThinkSono Guidance is currently only CE IIb marked for clinical use in the EU and UK. It is not FDA approved. Please contact ThinkSono for more information. The CE Class IIb intended purpose of ThinkSono Guidance is found in the instructions for use (IFU). ThinkSono Guidance EU manufacturer is ThinkSono GmbH (EUDAMED SRN number: DE-MF-000034914). To buy and use ThinkSono Guidance please send an email to hello@thinksono.com

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