Deep Learning Assisted Scoring of Point of Care Lung Ultrasound for Acute Decompensated Heart Failure in the Emergency Department

自新冠肺炎大流行爆发以来，北京地区入院的患者实行了登机治疗。 急诊科(ED)达到了前所未有的水平。对于危重病人，包括那些患有 急性失代偿性心力衰竭(ADHF)，ED寄宿会恶化预后，因为患者在急诊室呆上几个小时 等待转到适当的住院病房接受专门护理。鉴于...的增长有增无减 ED寄宿、ED停留时间以及随后接受专家评估和管理的时间，开发新的 能够在这些长时间的ED停留期间快速重新评估ADHF患者的技术对于 改善护理和患者结局。在急诊科的典型工作流程中，医生执行 在患者首次出现时，床边进行一次肺部超声检查，并利用有无B- 图像中的线条是肺充血的生物标志。通常由急诊医生以二进制形式进行评估 在急性ADHF中，B型线的存在与临床检查和血液检测相结合。 虽然检测B线可以像查看两个肺区一样容易地做出ADHF的临床决定，但计数 B线需要识别B线的技能和培训，以及汇总超过8个肺的B线计数 精确度区域。对于忙碌的急诊科医生来说，考虑到时间、培训和认知方面的限制，这是令人望而却步的 装填。为了缓解这一问题，急诊科医生需要能够自动计数和汇总B线的工具 量化拥堵的严重程度。如果没有这种自动化，完全有可能出现次优或 急诊室的ADHF患者甚至不会开始治疗，从而导致住院时间增加，进一步 使艾德登机永久化。创建用于自动量化的工具有可能实现 具有重新评估的工作流程，以满足不断变化的患者护理需求。我们的长期目标是发展 减轻超声图像采集和处理中普遍存在的对操作员的依赖的计算工具 释义。这个Trailblazer R21应用程序的目标是开发和验证计算方法 用于在急诊室通过床边肺超声量化肺充血，这将通过(1) 开发和评估可解释的工具，用于自动量化肺充血 回溯性肺超声数据以及(2)在工作流中验证训练模型的性能 由一项前瞻性观察研究证实，在这项研究中，向急诊室提出ADHF的患者将是 在治疗前和治疗后都用肺超声进行评估，结果通常用于测量 两个时间点都将记录住院服务中的肺部充血情况。

Since the onset of the COVID-19 pandemic, the practice of “boarding” patients admitted to the hospital in the Emergency Department (ED) has reached unprecedented levels. For critically ill patients including those with acute decompensated heart failure (ADHF), ED boarding worsens outcomes as patients spend hours in the ED waiting to be transferred to the appropriate inpatient ward for specialized care. Given the unabated increase in ED boarding, length of ED stay, and subsequent time to specialist evaluation and management, developing new technologies to enable rapid reassessment of ADHF patients during these protracted ED stays is critical for improved care and patient outcomes. In a typical workflow in the Emergency Department, physicians perform bedside lung ultrasound once, at time of initial patient presentation, and use the presence or absence of ‘B- Lines’ in the images as a biomarker for pulmonary congestion. Often assessed by ED physicians in a binary manner, the presence of B-lines is used in conjunction with a clinical exam and blood tests to rule in acute ADHF. While detecting B-lines can be as easy as looking at two lung zones to make a clinical decision of ADHF, counting B-lines requires both skill and training in B-line identification, and in aggregating B-line counts over 8+ lung zones for accuracy. For a busy ED physician this is prohibitive given constraints on time, training, and cognitive load. To ease this problem, ED physicians need tools that can automatically count and aggregate the B-lines to quantify the severity of the congestion. Without this automation, it is entirely possible that either suboptimal or even no treatment will be initiated for ADHF patients in the ED leading to increased hospital length of stay, further perpetuating the ED boarding. The creation of tools for automatic quantification has the potential to enable workflows with reassessment to meet the changing patient care needs. Our long-term goals are to develop computational tools that mitigate the operator-dependence endemic to ultrasound image acquisition and interpretation. The objective of this Trailblazer R21 application is to develop and validate computational methods for quantifying pulmonary congestion from bedside lung ultrasound in the ED, which will be achieved by (1) developing and evaluating explainable tools for automated quantification of pulmonary congestion using retrospective lung ultrasound data and (2) validating the performance of the trained models in a workflow demonstrated by a prospective observational study in which patients presenting to the ED with ADHF will be assessed with lung ultrasound both pre-and post-therapeutic intervention, and findings typically used to measure pulmonary congestion on inpatient services will be recorded for both time points.