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Focused Assessment with Sonography in Trauma (FAST) exam is the standard of care for pericardial and abdominal free fluid detection in emergency medicine. Despite its life saving potential, FAST is underutilized due to requiring clinicians with appropriate training and practice. To aid ultrasound interpretation, the role of artificial intelligence has been studied, while leaving room for improvement in localization information and computation time. The purpose of this study was to develop and test a deep learning approach to rapidly and accurately identify both the presence and location of pericardial effusion on point-of-care ultrasound (POCUS) exams. Each cardiac POCUS exam is analyzed image-by-image via the state-of-the-art YoloV3 algorithm and pericardial effusion presence is determined from the most confident detection. We evaluate our approach over a dataset of POCUS exams (cardiac component of FAST and ultrasound), comprising 37 cases with pericardial effusion and 39 negative controls. Our algorithm attains 92% specificity and 89% sensitivity in pericardial effusion identification, outperforming existing deep learning approaches, and localizes pericardial effusion by 51% Intersection Over Union with ground-truth annotations. Moreover, image processing demonstrates only 57 ms latency. Experimental results demonstrate the feasibility of rapid and accurate pericardial effusion detection from POCUS exams for physician overread. [ABSTRACT FROM AUTHOR]

Objectives: Emergency ultrasound (EUS) is a critical component of emergency medicine (EM) resident education. Currently, there is no consensus list of competencies for EUS training, and graduating residents have varying levels of skill and comfort. The objective of this study was to define a widely accepted comprehensive list of EUS competencies for graduating EM residents through a modified Delphi method. Methods: We developed a list of EUS applications through a comprehensive literature search, the American College of Emergency Physicians list of core EUS benchmarks, and the Council of Emergency Medicine Residency-Academy of Emergency Ultrasound consensus document. We assembled a multi-institutional expert panel including 15 faculty members from diverse practice environments and geographical regions. The panel voted on the list of competencies through two rounds of a modified Delphi process using a modified Likert scale (1 = not at all important, 5 = very important) to determine levels of agreement for each application--with revisions occurring between the two rounds. High agreement for consensus was set at >80%. Results: Fifteen of 15 panelists completed the first-round survey (100%) that included 359 topics related to EUS. After the first round, 195 applications achieved high agreement, four applications achieved medium agreement, and 164 applications achieved low agreement. After the discussion, we removed three questions and added 13 questions. Fifteen of 15 panelists completed the second round of the survey (100%) with 209 of the 369 applications achieving consensus. Conclusion: Our final list represents expert opinion on EUS competencies for graduating EM residents. We hope to use this consensus list to implement a more consistent EUS curriculum for graduating EM residents and to standardize EUS training across EM residency programs. [ABSTRACT FROM AUTHOR]

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B-lines assessed by lung ultrasound (LUS) outperform physical exam, chest radiograph, and biomarkers for the associated diagnosis of acute heart failure (AHF) in the emergent setting. The use of LUS is however limited to trained professionals and suffers from interpretation variability. The objective was to utilize transfer learning to create an AI-enabled software that can aid novice users to automate LUS B-line interpretation. Data from an observational AHF LUS study provided standardized cine clips for AI model development and evaluation. A total of 49,952 LUS frames from 30 patients were hand scored and trained on a convolutional neural network (CNN) to interpret B-lines at the frame level. A random independent evaluation set of 476 LUS clips from 60 unique patients assessed model performance. The AI models scored the clips on both a binary and ordinal 0–4 multiclass assessment. A multiclassification AI algorithm had the best performance at the binary level when applied to the independent evaluation set, AUC of 0.967 (95% CI 0.965–0.970) for detecting pathologic conditions. When compared to expert blinded reviewer, the 0–4 multiclassification AI algorithm scale had a reported linear weighted kappa of 0.839 (95% CI 0.804–0.871). The multiclassification AI algorithm is a robust and well performing model at both binary and ordinal multiclass B-line evaluation. This algorithm has the potential to be integrated into clinical workflows to assist users with quantitative and objective B-line assessment for evaluation of AHF. [ABSTRACT FROM AUTHOR]