determining the validity of thinksono guidance for ultrasound image acquisition and remote diagnosis

Conditions: Deep Vein Thrombosis; Deep Vein Thrombosis, Pulmonary Embolus

Summary: 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.

deep vein thrombosis (dvt) is well recognized to cause significant morbidity and mortality both at the time of diagnosis and post-diagnosis. with an estimated incidence of 1- 2 per 1,000 people, over to 300,000 people in the us will be diagnosed with venous thromboembolism (vte) per annum and two-thirds of these will be dvt. however, positive cases only represent 12-25% of the total number of patients who present with suspected dvt. in other words, between 75-88% of suspected dvt cases, when fully investigated, are negative. estimates have placed the yearly cost of vte to the us healthcare system at up to $10 billion. it is notoriously difficult to diagnose a dvt by clinical acumen alone. the standard approach to making a diagnosis of proximal dvt currently involves an algorithm combining pre-test probability assessment through the well's score, and compression ultrasonography. handheld ultrasound probes have recently become available. these probes have enabled 'app-based' compression ultrasonography to be performed without the need for bulky cart or laptop-based ultrasound machines. these new machines have a small form factor, meaning only the ultrasound probe is required for diagnostic purposes in conjunction with a smartphone or tablet. at present, although the new handheld probes are smaller and are better suited for point of care diagnosis, they still require an experienced radiologist or sonographer to perform the compression exam. this means that these devices can only be used wherever sonographers/radiologists are based most often i.e. hospital radiology departments. however, due to recent advances in "machine learning", software is now being developed for these 'app-based' probes that can assist non-radiology specialist healthcare professionals (e.g. nurses, non-radiologist physicians, general practitioners and other allied healthcare professionals) to carry out the compression ultrasound exam with minimal training. the thinksono guidance system is a guidance software expected to help non-radiology specialist healthcare professionals produce compression ultrasound image data that meet or exceed the minimal image quality criteria for a remote diagnosis by an expert (e.g. radiologist). the thinksono system is ce mark approved in the european union and in clinical use in europe. this study is a multi-site non-randomized, double-blinded, prospective cohort pivotal study. the purpose of this study is to confirm the safety and efficacy of the thinksono guidance system as per the intended use defined as: thinksono guidance system is a guidance, data acquisition and communication tool that guides non-ultrasound-trained healthcare staff to collect point of care compression ultrasound data in the proximal deep venous system of the lower extremity for interpretation by qualified clinicians.

Eligibility criteria:
inclusion criteria: * the participant is willing to provide written informed consent to participate in this research. * the participant is over the age of 18 * the participant has symptoms suggestive of a deep venous thrombosis (dvt) * the diagnostic dvt algorithm indicates that an ultrasound is needed 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.

Keywords: deep vein thrombosis artificial intelligence machine learning ultrasound