ThinkSono System for Deep Vein Thrombosis
(DVT GUARD Trial)
Recruiting in Palo Alto (17 mi)
+3 other locations
Overseen byGlenn Jacobowitz, MD
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: ThinkSono, Ltd.
Disqualifiers: Consent not given, Incomplete scan, others
No Placebo Group
Approved in 1 Jurisdiction
Trial Summary
What is the purpose of this trial?The purpose of this study is to confirm the safety and efficacy of the ThinkSono Guidance System, a software data collection and communication tool designed to collect ultrasound data to help detect blood clots in veins. The ThinkSono system is CE Mark approved in the European Union and in clinical use in Europe. Usually, when an ultrasound is conducted to diagnose blood clots in veins, a sonographer (trained technologist who conducts ultrasounds) and/or radiologist will conduct the procedure, including a compression ultrasound exam, and the scan may require a bulky cart and ultrasound equipment. The ThinkSono Guidance System is a mobile software application that enables other healthcare professionals such as nurses, non-radiologist physicians including general practitioners, and other allied healthcare professionals to perform the ultrasound at the point of care using guidance from the software app. This is a multi-site non-randomized, double-blinded, prospective cohort pivotal study.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the ThinkSono Guidance System treatment for deep vein thrombosis?
The ThinkSono Guidance System uses a deep learning approach to help non-specialists diagnose deep vein thrombosis (DVT) using ultrasound images. In a study, this method showed high accuracy, with a sensitivity (ability to correctly identify those with the condition) of up to 94% and a negative predictive value (likelihood that those identified as not having the condition truly don't have it) of up to 100%, making it a promising tool for diagnosing DVT.
12345How does the ThinkSono Guidance System treatment for deep vein thrombosis differ from other treatments?
The ThinkSono Guidance System is unique because it uses machine learning to help non-specialists diagnose deep vein thrombosis (DVT) at the point of care by interpreting ultrasound images, potentially reducing the need for specialist referrals and long waiting times.
56789Eligibility Criteria
This trial is for individuals who may have blood clots in their veins, such as those with pulmonary embolism or deep vein thrombosis. It's open to patients where healthcare professionals suspect a clot and need an ultrasound diagnosis. Specific eligibility criteria are not provided, but typically include adults who consent to participate.Inclusion Criteria
The diagnostic DVT algorithm indicates that an ultrasound is needed
The participant is willing to provide written informed consent to participate in this research
I am over 18 years old.
+1 more
Exclusion Criteria
Patient consent not given or retracted during the study
Local imaging specialists fail to scan the patient or fail to produce a conclusive imaging diagnosis
Incomplete ThinkSono Guidance scan due to logistical or other issues such as pain, lack of patient cooperation, barriers such as a cast or other physical limitations
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Data Collection
Participants undergo ultrasound scans using the ThinkSono system and a comparison standard of care duplex ultrasound scan
8 months
Multiple visits as required for data collection
Follow-up
Participants are monitored for safety and effectiveness after data collection
4 weeks
Participant Groups
The ThinkSono System, a mobile app designed to guide healthcare providers through conducting ultrasounds for blood clot detection at the point of care, is being tested for its safety and effectiveness compared to traditional methods.
1Treatment groups
Experimental Treatment
Group I: Comparison ArmExperimental Treatment1 Intervention
This arm of patients will undergo an ultrasound scan using the ThinkSono system and a comparison standard of care duplex ultrasound scan.
ThinkSono Guidance System is already approved in European Union for the following indications:
🇪🇺 Approved in European Union as ThinkSono Guidance for:
- Detection of deep vein thrombosis (DVT)
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
NYU Langone HealthNew York, NY
Temple HealthPhiladelphia, PA
Allegheny Health NetworkPittsburgh, PA
South Texas Veterans Health SystemSan Antonio, TX
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Who Is Running the Clinical Trial?
ThinkSono, Ltd.Lead Sponsor
NYU Langone HealthCollaborator
Temple HealthCollaborator
Allegheny Health NetworkCollaborator
Allegheny Health NetworkCollaborator
References
Clinical decision support systems to improve utilization of thromboprophylaxis: a review of the literature and experience with implementation of a computerized physician order entry program. [2012]A literature review was conducted of studies investigating the effectiveness of paper- and computer-based clinical decision support systems (CDSS) used with or without educational programs designed to increase the use of venous thromboembolism (VTE) prophylaxis.
The early management of DVT in the North West of England: A nation-wide problem? [2015]Despite NICE guidelines, the early management of deep vein thrombosis (DVT) in UK hospitals varies widely. We investigated the variation in clinical pathways used in NHS hospitals in North West England.
Implementation and evaluation of practice guidelines. [2018]Practice guidelines for the management of deep vein thrombosis were implemented in a Problem-Oriented Patient Management System on the HELP Hospital Information System at the LDS Hospital. A hierarchical knowledge representation was used. The Problem-Oriented Patient Management System was designed to generate patient-specific guideline suggestions according to the clinical situation at the time of generation. A retrospective evaluation was used to compare the appropriateness of the generated guideline suggestions to the appropriateness of the attending physician's management decisions. A significantly higher proportion of guideline suggestions was evaluated to be appropriate, compared to the proportion of attending physician's management decisions found to be appropriate.
A clinical decision support system for venous thromboembolism prophylaxis at a general hospital in a middle-income country. [2021]To determine the impact that implementing a combination of a computer-based clinical decision support system and a program of training seminars has on the use of appropriate prophylaxis for venous thromboembolism (VTE).
Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. [2023]Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.
Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection. [2021]Using a wearable electromyography (EMG) and an accelerometer sensor, classification of subject activity state (i.e., walking, sitting, standing, or ankle circles) enables detection of prolonged "negative" activity states in which the calf muscles do not facilitate blood flow return via the deep veins of the leg. By employing machine learning classification on a multi-sensor wearable device, we are able to classify human subject state between "positive" and "negative" activities, and among each activity state, with greater than 95% accuracy. Some negative activity states cannot be accurately discriminated due to their similar presentation from an accelerometer (i.e., standing vs. sitting); however, it is desirable to separate these states to better inform the risk of developing a Deep Vein Thrombosis (DVT). Augmentation with a wearable EMG sensor improves separability of these activities by 30%.
Detection of deep venous thrombosis by magnetic resonance imaging. [2022]To determine the accuracy of gradient recalled echo magnetic resonance imaging in assessing deep venous thrombosis.
[D-Dimer determination combined with clinical probability for the diagnosis of leg venous thrombosis]. [2014]To evaluate the results of combination of D-Dimer test and simple clinical model for the diagnosis of deep vein thrombosis (DVT).
Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images. [2022]In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient's veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May-Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable (p < 0.005). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.