Abstract: In Progress: A prospective clinical implementation of AutoDVT in hospital DVT patient assessment settings (under research license). Key endpoints under measurement include data collection pathway improvement, clinical pathway health economic assessment, and quality of data capture by non-specialist healthcare practitioners (e.g. nurses). Participating NHS trusts include Oxford, Leicester, Sheffield, King’s, Barts, Buckinghamshire, St. George’s, Surrey & Cardiff. Estimated completion: late 2022.

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.

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 …

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.

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.

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.