FIrst REsponse BUrn Diagnostic System (FIRE-BUDS)

第一反应锅炉诊断系统（FIRE-BUDS） 项目摘要 烧伤引起的发病率和死亡率可以大大降低， 准确评估伤情。大约有5-6%的患者在医疗机构接受治疗 呈现烧伤不能存活，并且在这些病例中的46%中，感染是烧伤的主要原因。 死亡烧伤评估包括深度分类、总体表面积（%TBSA）和 随后的治疗决定，包括最重要的一个：受伤是否需要 手术与否。理想情况下，建议的治疗应由经验丰富的烧伤专家提供， 专门的烧伤机构然而，除了少数几个经过验证的烧伤中心外， 美方引导式体格检查沿着自动烧伤评估是一种有吸引力的 一种比当前烧伤评估程序更实用和准确的替代方法 由非专业从业人员在严峻的环境中进行。 我们的目标是将AI和物理动作融入我们的便携式系统，以方便评估 和患者的预后。这种应用程序将能够识别和执行自动 分割和分类，以确定是否需要手术，并提供烧伤转换 预报.除了从图像中获得的信息外，Harmonic B模式超声 （HUSD）和谐波组织多普勒弹性成像（TDI）的损伤，它将引导 医生通过使用触觉和其他物理手段来评估 损伤（例如，压迫时发白、针刺感和针刺出血）和通过 自然对话处理。我们将通过以下具体目标来实现我们的目标：1）创建一个 使用临床图像、HUSD和TDI视频的猪模型中的烧伤数据库; 2）开发 使用人工智能技术组合的分割、引导评估和预测算法 和协作动作; 3）在用户研究中对自动化移动的应用进行建模。方法：我们 将预处理和组织先前收集的猪多处烧伤数据 模型，并使用在线工具进行标签过程。我们将使用Mask R-CNN进行分割 任务，自然语言处理（NLP）和计算机视觉的指导评估任务。我们 将使用AI技术获得我们系统的每种不同输入方式的特征， 将它们连接起来并训练SVM分类器用于深度分类任务。然后，我们将使用 燃烧转化预测任务的异常检测方法。我们将测试 该系统在用户研究中使用了更多具有多处烧伤的猪受试者。的结果 研究将有助于援助从业人员和烧伤病人，改善烧伤的结果， 在没有烧伤专家的情况下，此外，我们提出了一个框架，能够 支持关于手术要求的医疗决策过程，并生成 强大的预测，可以使新的医疗应用的急诊医学，其中 治疗的决定可以受益于强大的智能技术。

FIrst REsponse BUrn Diagnostic System (FIRE-BUDS) PROJECT SUMMARY Morbidity and mortality rates resulting from burn injuries can be drastically reduced with prompt and accurate assessment of the injury. Approximately, 5-6% of the patients admitted to a medical facility presenting burns does not survive, and in the 46% of these cases, infection is the leading cause of death. Burn assessment includes depth classification, total body surface area (%TBSA), and subsequent treatment decisions, including the most important one: whether the injury requires surgery or not. Ideally, the suggested treatment should be provided by an experienced burn expert in a specialized burn facility. However, burn experts are scarce beyond the few verified burn centers in the US. Guided physical examination along with automated burn assessment is an attractive alternative that can be more practical and accurate than the current burn assessment procedure performed by non-expert practitioners in austere environments. Our goal is to incorporate AI and physical action into our portable system to facilitate the assessment and prognosis of the patient. Such application would be able to identify and perform automatic segmentation and classification, to determine if surgery is needed, and offer a burn conversion forecast. In addition to the information obtained from the image, the Harmonic B-mode Ultrasound (HUSD), and the Harmonic Tissue Doppler Elastography Imaging (TDI) of the injury, it will guide the practitioner through the diagnostic process using tactile and other physical means for assessing the injury (e.g. blanching to pressure, sensation to pin prick and bleeding on needle prick) and through natural dialogue processing. We will achieve our goal through the following Specific Aims: 1) Create a database of burn injuries in porcine models using clinical images, HUSD and TDI videos; 2) Develop algorithms for segmentation, guided assessment, and prediction using a combination of AI techniques and collaborative action; 3) Validate the automated mobile application in a user study. Methods: We will preprocess and organize data collected previously of multiple burn injuries generated in porcine models, and use online tools for the labelling process. We will use Mask R-CNN for the segmentation task, Natural Language Processing (NLP) and Computer Vision for the guided assessment task. We will obtain features for each of the different input modalities of our system using AI techniques to concatenate them and train an SVM classifier for the depth classification task. Then, we will use an anomaly detection approach for the burn conversion prediction task. We will test the performance of the system using more pig subjects with multiple burn injuries in a user study. The results of this research will contribute to aid practitioners and burn patients, improving the outcomes of a burn injury, even in the absence of burn experts. Moreover, we propose a framework that is capable of supporting the medical decision-making process regarding the surgical requirements, and generating robust forecasts that can enable new medical applications for emergency medicine where the decision of the treatment can benefit from robust intelligence techniques.