FIrst REsponse BUrn Diagnostic System (FIRE-BUDS)

第一响应烧伤诊断系统 (FIRE-BUDS)

• 批准号: 10392084
• 负责人: Gayle M Gordillo
• 金额: $ 21.19万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10392084
• 关键词: Accounting; Address; Affect; Algorithms; Appearance; Artificial Intelligence; Body Surface Area; Burn Centers; Burn injury; Categories; Cause of Death; Characteristics; Cicatrix; Classification; Clinical; Complex; Computer Vision Systems; Consensus; Cues; Data; Data Set; Databases; Decision Making; Decision Support Systems; Detection; Development; Diagnostic; Early treatment; Emergency Medicine; Environment; Esthesia; Family suidae; Goals; Hemorrhage; Hour; Human; Image; Infection; Infection prevention; Injury; Intelligence; Label; Light; Linear Models; Machine Learning; Masks; Mechanics; Medical; Medical Care Costs; Medical Imaging; Methods; Modality; Modeling; Morbidity - disease rate; Natural Language Processing; Needles; Operative Surgical Procedures; Palpation; Patients; Performance; Persons; Physical Examination; Physical assessment; Probability; Procedures; Process; Process Assessment; Reconstructive Surgical Procedures; Research; Severities; Smell Perception; Specialist; Statistical Data Interpretation; Subcutaneous Tissue; System; Tactile; Techniques; Temperature; Testing; Time; Tissues; Training; Traumatic injury; United States; Visual; automated segmentation; base; burn therapy; burn wound; clinical imaging; diagnostic platform; elastography; experience; hemodynamics; image processing; improved; improved outcome; interest; mobile application; mortality; operation; optimal treatments; patient prognosis; performance tests; porcine model; portability; pressure; response; rural area; second degree burn; segmentation algorithm; skin color; statistics; third degree burn; tool; ultrasound; visual information; wound dressing; wound healing

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.

首先响应烧伤诊断系统（火炉） 项目摘要 烧伤造成的发病率和死亡率可以通过及时及时大幅降低 准确评估伤害。大约有5-6％的患者进入医疗机构 出现烧伤无法生存，在这些情况的46％中，感染是 死亡。烧伤评估包括深度分类，全身表面积（％TBSA）和 随后的治疗决策，包括最重要的决定：伤害是否需要 是否手术。理想情况下，建议的治疗应由经验丰富的烧伤专家提供 专门的烧伤设施。但是，燃烧专家的稀缺超出了少数经过验证的燃烧中心 美国。指导体格检查以及自动烧伤评估是一个有吸引力的 与当前的燃烧评估程序相比，可以更实用和准确的替代方案 由非专家从业者在严峻的环境中执行。 我们的目标是将AI和身体行动纳入我们的便携式系统，以促进评估 和患者的预后。此类应用程序将能够识别和执行自动 细分和分类，以确定是否需要手术，并提供燃烧转换 预报。除了从图像获得的信息外，谐波B模式超声 （胡子）和伤害的谐波组织多普勒弹性成像（TDI），它将指导 通过诊断过程使用触觉和其他物理手段来评估诊断过程 伤害（例如，压力浮出水面，刺痛和刺痛的感觉）和通过 自然对话处理。我们将通过以下特定目标来实现我们的目标：1）创建一个 使用临床图像，胡子和TDI视频中的猪模型中烧伤伤害的数据库； 2）发展 使用AI技术的组合，用于分割，指导评估和预测的算法 和协作行动； 3）在用户研究中验证自动移动应用程序。方法：我们 将预处理和组织以前收集的数据 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 指导评估任务的任务，自然语言处理（NLP）和计算机视觉。我们 将使用AI技术来获得系统的每种不同输入方式的功能 将它们连接并训练SVM分类器进行深度分类任务。然后，我们将使用 燃烧转换预测任务的异常检测方法。我们将测试性能 在用户研究中，该系统使用更多的猪受试者进行多次烧伤。结果的结果 研究将有助于援助从业者和烧伤患者，改善烧伤的结果 受伤，即使没有燃烧专家。此外，我们提出了一个能够 支持有关手术要求的医疗决策过程，并产生 强大的预测可以为急诊医学提供新的医疗应用 治疗的决定可以受益于健壮的智能技术。