Modern point of care ultrasound (POCUS) devices can perform echocardiograms from a smartphone, greatly improving accessibility. However, operator expertise is still required to gather high quality data which is needed to accurately view and diagnose patients. The goal of this study is to enhance the collection of mobile ultrasound echocardiograms with AI machine learning. AI can provide feedback to a POCUS operator to help maximize clinical usability of data. To realize this, we used the Intel GETi framework to create computer vision models that quantify the readability of frames taken from an echocardiogram. These models determine the quality and the orientation of each frame. Feedback from these models can alert the user to proper positioning and technique to gather usable ultrasound data. Tests on existing data show the accuracy of the models ranging from 77%-99%. As the GETi framework develops further, it has the potential to perform these tests in real time from a mobile device.
Flexible ureteroscopes (fURS) are the most commonly used surgical device for endoscopic management of upper urinary tract conditions, including nephrolithiasis. Single-use flexible ureteroscopes (su-fURS) were introduced in 2015 with purported decrease maintenance costs and sterility concerns compared to reusable devices; however, the ergonomic impact of these devices on surgeons is not well-characterized. This study aims to investigate su-fURS ergonomics by developing a biomechanical feedback system for use during ureteroscopy. The study is designed for use by numerous su-fURS, with an initial focus on LithoVue™ (Boston Scientific). Two experimental models mimicking in-vivo fURS use were selected: an anatomically correct kidney-ureter-bladder (KUB) model; and a fURS training model simulating varying physical complexity and operator strain. Clinically relevant testing metrics selection was informed by fellowship-trained endourologist consultation. A series of representative fURS tasks was developed for testing: endoluminal navigation, kidney stone manipulation, basketing, and extraction. The dominant hand thumb, index finger distal interphalangeal joint, extensor digitorum tendons, and flexor digitorum muscle were identified as most relevant for monitoring and highest strain risk during fURS operation. A biomechanical feedback system was developed using a prototypical set of inertial measurement units provided by Mayo Clinic special purpose processor development group to provide live readings during endoscopic movements. Pilot testing demonstrated reliable hand kinematic measurements during simulated ureteroscopy. We developed and pilot-tested a novel biomechanical feedback system for flexible ureteroscopy to provide live feedback during ureteroscopy. Following further validation, this system may be applied to improve surgical training, decreasing physician fatigue and injury, and ultimately improve patient care.
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