Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma.
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ABSTRACT:
SUBMITTER: Chiu IM
PROVIDER: S-EPMC10051044 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
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Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma.
Chiu I-Min IM Lin Chun-Hung Richard CR Yau Fei-Fei Flora FF Cheng Fu-Jen FJ Pan Hsiu-Yung HY Lin Xin-Hong XH Cheng Chi-Yung CY
JAMA network open 20230301 3
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Project description: Not available
| S-EPMC7807983 | biostudies-literature
Project description:Focused assessment with sonography for trauma (FAST) is a part of resuscitation of trauma patients recommended by international panel consensus. The purpose of FAST is to identify free fluid, which necessarily means blood in acute trauma patients. In this article, the authors focused on various aspects of FAST in the emergency department, prehospital care, pediatric setting, training and general pearls/pitfalls. Detailed techniques and interpretation of FAST are beyond the scope of this article.
| S-EPMC5536884 | biostudies-other
Project description:ObjectivePediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician-performed pediatric FAST.MethodsWe developed and conducted a retrospective cohort analysis of a deep learning view classifier on real-world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians. FAST was randomly distributed to training, validation, and test datasets, 70:20:10; each child was represented in only one dataset. The primary outcome was view classifier accuracy for video clips and still frames.ResultsThere were 699 FAST studies, representing 4925 video clips and 1,062,612 still frames, performed by 30 different clinicians. The overall classification accuracy was 97.8% (95% confidence interval [CI]: 96.0-99.0) for video clips and 93.4% (95% CI: 93.3-93.6) for still frames. Per view still frames were classified with an accuracy: 96.0% (95% CI: 95.9-96.1) cardiac, 99.8% (95% CI: 99.8-99.8) pleural, 95.2% (95% CI: 95.0-95.3) abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) suprapubic.ConclusionA deep learning classifier can accurately predict pediatric FAST views. Accurate view classification is important for quality assurance and feasibility of a multi-stage deep learning FAST model to enhance the evaluation of injured children.
| S-EPMC9072593 | biostudies-literature
Project description:ImportanceArtificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.ObjectiveTo test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.Design, setting, and participantsThis prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.InterventionsEach patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition.Main outcomes and measuresFour primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.ResultsA total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.Conclusions and relevanceThis DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.
| S-EPMC8204203 | biostudies-literature
Project description:BackgroundMastery learning has gained popularity for training residents in procedural skills due to its demonstrated superiority over traditional methods. However, no studies have compared the efficacy of traditional versus mastery learning methods in residency point-of-care ultrasound education. We hypothesized that mastery learning would improve residents' skills in performing the extended focused assessment with sonography in trauma (eFAST).MethodsAll first-year emergency medicine (EM) resident physicians at a single university hospital underwent a crossover randomized controlled trial to receive mastery-learning eFAST training either at the beginning of the academic year or 6 months into intern year. Participants were taught using a checklist validated by a panel of experts using Mastery Angoff methods and were given feedback on missed tasks until each trainee completed the eFAST with a minimum passing standard (MPS). Our primary outcome was technical proficiency between the two groups for eFAST examinations performed in the emergency department during the academic year.ResultsSixteen interns were enrolled; eight were randomized to each group. The group that received mastery training at the beginning of the year had mean clinical eFAST proficiency scores above the MPS in the first two quarters of the academic year, while the control group did not. Once the control group underwent eFAST mastery training at the midpoint of the year, both groups had mean proficiency scores above the MPS for the remainder of the year.ConclusionSimulation-based mastery learning is an effective method of teaching the eFAST examination. This training during intern orientation conferred early proficiency in clinical performance of eFAST among EM residents. This difference in proficiency was no longer present after the control group received mastery learning education halfway through the academic year.
| S-EPMC8190510 | biostudies-literature
Project description:Liver trauma is a recognised rare complication of cardiopulmonary resuscitation (CPR) and may be difficult to detect. We report a case of intraperitoneal haemorrhage due to liver injury following CPR in a 50-year-old man admitted to the intensive care unit. The haemorrhage was diagnosed with focused assessment with sonography for trauma (FAST). FAST can rapidly and easily diagnose liver injury. FAST is recommended for excluding haemoperitoneum in patients who are haemodynamically unstable after resuscitation.
| S-EPMC5535084 | biostudies-other
Project description:ObjectivesComputed tomography (CT), the reference standard for diagnosis of intraabdominal injury (IAI), carries risk including ionizing radiation. CT-sparing clinical decision rules for children have relied heavily on physical examination, but they did not include focused assessment with sonography for trauma (FAST), which has emerged into widespread use during the past decade. We sought to determine the independent associations of physical examination, laboratory studies, and FAST with identification of IAI in children and to compare the test characteristics of these diagnostic variables. We hypothesized that FAST may add incremental utility to a physical examination alone to more accurately identify children who could forgo CT scan.MethodsWe reviewed a large trauma database of all children with blunt torso trauma presenting to a freestanding pediatric emergency department during a 20-month period. We used logistic regression to evaluate the association of FAST, physical examination, and selected laboratory data with IAI in children, and we compared the test characteristics of these variables.ResultsAmong 354 children, 50 (14%) had IAI. Positive FAST (odds ratio [OR] = 14.8, 95% confidence interval [CI] = 7.5 to 30.8) and positive physical examination (OR = 15.2, 95% CI = 7.7 to 31.7) were identified as independent predictors for IAI. Physical examination and FAST each had sensitivities of 74% (95% CI = 60% to 85%). Combining FAST and physical examination as FAST-enhanced physical examination (exFAST) improved sensitivity and negative predictive value (NPV) over either test alone (sensitivity = 88%, 95% CI = 76% to 96%) and NPV of 97.3% (95% CI = 94.5% to 98.7%).ConclusionsIn children, FAST and physical examinations each predicted the identification of IAI. However, the combination of the two (exFAST) had greater sensitivity and NPV than either physical examination or FAST alone. This supports the use of exFAST in refining clinical predication rules in children with blunt torso trauma.
| S-EPMC8502256 | biostudies-literature
Project description:ImportanceThe wide variation in the accuracy and reliability of the Focused Assessment With Sonography for Trauma (FAST) and the extended FAST (E-FAST) for children after blunt abdominal trauma reflects user expertise. FAST and E-FAST that are performed by experts tend to be more complete, better quality, and more often clinically valuable.ObjectiveTo develop definitions of a complete, high-quality, and accurate interpretation for the FAST and E-FAST in children with injury using an expert, consensus-based modified Delphi technique.Design, setting, and participantsThis consensus-based qualitative study was conducted between May 1 to June 30, 2021. It used a scoping review and iterative Delphi technique and involved 2 rounds of online surveys and a live webinar to achieve consensus among a 26-member panel. This panel consisted of international experts in pediatric emergency point-of-care ultrasonography.Main outcomes and measuresDefinitions of complete, high-quality, and accurate FAST and E-FAST studies for children after injury.ResultsOf the 29 invited pediatric FAST experts, 26 (15 men [58%]) agreed to participate in the panel. All 26 panelists completed the 2 rounds of surveys, and 24 (92%) participated in the live and asynchronous online discussions. Consensus was reached on FAST and E-FAST study definitions, and the panelists rated these 5 anatomic views as important and appropriate for a complete FAST: right upper-quadrant abdominal view, left upper-quadrant abdominal view, suprapubic views (transverse and sagittal), and subxiphoid cardiac view. For E-FAST, the same FAST anatomic views with the addition of the lung or pneumothorax view were deemed appropriate and important. In addition, the panelists rated a total of 32 landmarks as important for assessing completeness. Similarly, the panelists rated 14 statements on quality and 20 statements on accurate interpretation as appropriate.Conclusions and relevanceThis qualitative study generated definitions for complete FAST and E-FAST studies with high image quality and accurate interpretation in children with injury. These definitions are similar to those in adults with injury and may be used for future education, quality assurance, and research. Future research may focus on interpretation of trace volumes of abdominal free fluid and the use of serial FAST.
| S-EPMC8933745 | biostudies-literature
Project description: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.
| S-EPMC10682760 | biostudies-literature
Project description:Malignant effusions occur frequently in patients with cancer and are important to diagnose and treat. In this report, we describe a novel point-of-care ultrasound (POCUS) protocol to rapidly identify pleural effusion, pericardial effusion, and ascites: The Focused Assessment with Sonography in Cancer (FASC). This protocol utilizes six standard sonographic positions to identify the presence of fluid in common anatomic spaces. The FASC examination is intended for widespread use by oncologists and other clinicians who treat patients with cancer.
| S-EPMC9979872 | biostudies-literature