An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Abstract

Introduction: Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST).Methods: The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models.Results: Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom.Discussion: Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

Design and testing of ultrasound probe adapters for a robotic imaging platform

“…Future testing will evaluate the ability of the robotic arm with probe holder attachments to access and capture quality images of relevant features at the proper eFAST scan points using a more robust model. Our group has developed an eFAST tissue phantom with positive and negative injury states at each scan site of the exam, that will allow to assess performance for the end-adapters while capturing eFAST relevant ultrasound images 8 . Future work will involve developing and testing a system for replicating such techniques using reinforcement learning or similar approaches to ensure feasibility of proper image acquisition by the robotic arm and probe adapters.…”

“…www.nature.com/scientificreports/ eFAST [5][6][7][8] , and it is the subject of many active research efforts [9][10][11] . In this work, we will focus on the image acquisition side of the ultrasound automation challenge.…”

Deep learning models for interpretation of point of care ultrasound in military working dogs

“…The last model used, ShrapML, was purpose built and Bayesian optimized for identifying shrapnel in ultrasound images at a high accuracy and much more rapid than conventional models. In addition, we have shown it to be successful at identifying pneumothorax, hemothorax, and abdominal hemorrhage injuries in eFAST images captured in human tissue phantom models ( 28 ). ShrapML consists of 8 convolutional layers with only 430,000 trainable parameters.…”

“…Our research team has previously developed an ultrasound image AI interpretation model for detecting shrapnel in tissue, termed ShrapML ( 26 , 27 ). We have recently expanded this work to the enhanced FAST (eFAST) exam commonly used for human emergency triage applications ( 28 ). This application resulted in different AI models for detecting pneumothorax, hemothorax, and abdominal hemorrhage injuries in tissue phantom image sets.…”

Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

“…Smart medicine applications have been extensively reviewed elsewhere [25][26][27][28][29][30]. Focusing on AI for interpreting ultrasound images [31], applications include the identification of tumors [32], diagnosing infectious disease [33,34], and determining eFAST scan outcomes [35,36], among others. Each of these applications often relies on deep convolutional neural networks, which extract image features and parameter weights to identify differences in images.…”