Standardizing
Tools for obstetrical exams that improve consistency and save time
Detecting
Speed up exam time and enable consistency with Auto Nipple Detection
Measuring
Ascertains measurement of strain in left ventricle in nine seconds
Validation
Immediate analysis of breast coverage and positioning to bolster confidence
Optimizing
Abdominal organ detection optimizes image quality and confidence
Populating
Get AI-based thyroid cancer risk assessment
Guidance
Real-time, on-screen guidance to get diagnostic-quality cardiac images
Labeling
Automatically labels liver, gallbladder and right kidney on images
Assessing
Automatically provides a quantitative breast malignancy risk assessment
Deep learning algorithms—trained on vast amounts of data—support consistent, high-quality imaging, no matter the experience level of the clinician performing the scan.
Guidance ultrasound opens the possibilities to acquire reliable, consistent, diagnostic-quality images and also capturing complex, complete ultrasound studies.
AI-enabled tools such as Caption AI and Caption Guidance provide cardiac scan guidance on the Vscan Air SL and the Venue family. This AI expands the possibility of full echocardiography at the point of care to patients who may not otherwise have access to ultrasound or may experience long wait times.
With this AI-driven software, ultrasound users can capture cardiac images successfully. Real-time, turn-by-turn, on-screen guidance helps users capture diagnostic-quality images with fewer repeat scans, helping to reduce costs.
With increased demand for imaging services showing no sign of slowing down and a continued tight labor market influenced by heavy workloads and burnout, organizations are to looking for ways to ensure that time is spent on the highest-value tasks.
90% of sonographers have experienced symptoms of work-related musculoskeletal disorders,3 and these injuries have led up to over $120 billion yearly in direct and indirect costs for employers.4 That's why AI is a critical tool when it comes to saving time and clicks during scans. This not only increases productivity but also eases the wear and tear on clinicians by reducing clicks and automating certain repetitive tasks.5, 6
Ultrasound is especially vulnerable to operator variability between exams,7 in addition to challenges with inter-reader variability. For example, when evaluating a thyroid nodule to determine if a biopsy is needed, radiologists with comparable training and experience disagree with each other 25% of the time making their diagnosis. In fact, when presented the same case a month later, physicians disagree with themselves, changing their initial diagnosis nearly 20% of the time.7
AI-enabled ultrasound has the power to exponentially add to a clinician’s knowledge, potentially improving patient outcomes, increased throughput, and reduced costs because of fewer repeat scans.
