News & Publications
Temple Health Joins U.S. Study of Artificial Intelligence Diagnostic System for Detection of Deep Vein Thrombosis
Temple Health Joins U.S. Study of Artificial Intelligence Diagnostic System for DVT Detection
Fouad Noor Presents ThinkSono at LSI Europe ’23
ThinkSono Partners With NYU Langone Health to Conduct Study of Ultrasound AI Guidance Software for DVT Detection
ThinkSono Joins Butterfly Garden to Deploy ThinkSono AI on Butterfly’s Imaging Platform
Simplifying DVT Diagnosis by Non-experts using AI-assisted Ultrasonography
ThinkSono Clinical Study Featured in Vein Therapy news
ThinkSono Selected as Top 96 AI Companies in CB Insights
ThinkSono AI is now available via Clarius Marketplace
The State of Ultrasound Technology with Dr. Greg Fritz and ThinkSono CEO Fouad Al-Noor
ThinkSono partners with Point-of-Care Ultrasound (POCUS) Certification Academy
ThinkSono Wins German Innovation Prize
ThinkSono Collaborates With University Of Lübeck
SpeedInvest Invested In ThinkSono
ThinkSono CEO Gave TedX Talk
ThinkSono Featured In Aunt Minnie
ThinkSono Celebrated For Tech Innovation In The State Of Brandenburg
ThinkSono Wins Falling Walls Berlin – Science Startups
ThinkSono Won the Wales – Precision medicine challenge
ThinkSono becomes EIT Health Finalist
Publications & News
Annals of Vascular Surgery
Remote Expert DVT Triaging of Novice-User Compression Sonography with AI-Guidance
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.
IN PROGRESS: Q3 2022
ADVENT: A Multi-Site Study to Validate The Efficacy of ThinkSono AI-Guidance for Use By Non-Specialist Practitioners in the UK
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.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTIONS – 2018
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.
NATURE DIGITAL MEDICINE (NPJ) – 2021
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 …
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.
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.
Journal of Vascular Surgery: Venous and Lymphatic Disorders
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.