FIrst REsponse BUrn Diagnostic System (FIRE-BUDS)

第一反应烧伤诊断系统(火苗) 项目总结 烧伤引起的发病率和死亡率可以通过及时和及时的 准确评估伤情。大约5%-6%的患者被送往医疗机构 目前的烧伤没有存活下来，在46%的这些病例中，感染是导致 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 不管是不是手术。理想情况下，建议的治疗应由经验丰富的烧伤专家提供 专门的焚烧设施。然而，除了少数几个经过验证的烧伤中心外，烧伤专家很少 美国。引导式体检和自动烧伤评估是一种有吸引力的 可以比目前的烧伤评估程序更实用和更准确的替代方案 由非专家从业者在严酷的环境中执行。 我们的目标是将人工智能和身体动作整合到我们的便携式系统中，以便于评估 以及患者的预后。这种应用将能够识别并自动执行 分段和分类，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，谐波B型超声 (HUS)和损伤的谐波组织多普勒弹性成像(TDI)，它将指导 医生通过诊断过程使用触觉和其他物理手段来评估 损伤(如压力烫伤、针刺感觉和针刺出血)和穿透 自然对话处理。我们将通过以下具体目标实现我们的目标：1)创建 使用临床图像、HUS和TDI视频的猪烧伤模型数据库；2)开发 结合使用人工智能技术的分割、指导评估和预测算法 和协作行动；3)在用户研究中验证自动化移动应用。方法：我们 我将对以前收集的猪多处烧伤的数据进行预处理和组织 模型，并使用在线工具进行标记过程。我们将使用MASK R-CNN进行分割 任务、自然语言处理(NLP)和计算机视觉用于指导性评估任务。我们 将使用人工智能技术获取我们系统的每种不同输入模式的功能 将它们连接起来，并为深度分类任务训练一个支持向量机分类器。然后，我们将使用 针对烧伤转化预测任务的异常检测方法。我们将测试它的性能 该系统在一项用户研究中使用了更多有多处烧伤的猪对象。这样做的结果 研究将有助于帮助从业者和烧伤患者，改善烧伤的结果 受伤，即使在没有烧伤专家的情况下也是如此。此外，我们提出了一个框架，该框架能够 支持有关手术要求的医疗决策过程，并生成 强大的预测，可以支持急救医学的新医疗应用 治疗的决策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.