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.

第一响应烧伤诊断系统 (FIRE-BUDS) 项目概要 通过及时和有效的救治，可以大大降低烧伤造成的发病率和死亡率 准确评估伤害。大约 5-6% 的患者入住医疗机构 出现烧伤的人无法存活，并且在 46% 的此类病例中，感染是导致烧伤的主要原因 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 专门的烧伤设施。然而，除了少数经过验证的烧伤中心外，烧伤专家很少。 美国。引导体检和自动烧伤评估是一个有吸引力的方法 比当前烧伤评估程序更实用、更准确的替代方案 由非专业从业者在严峻的环境中进行。 我们的目标是将人工智能和身体动作融入我们的便携式系统中，以促进评估 以及患者的预后。此类应用程序将能够识别并自动执行 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，谐波 B 型超声 (HUSD) 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（例如压力变白、针刺感觉和针刺出血）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 视频建立猪模型烧伤数据库； 2）开发 结合人工智能技术进行分割、引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化移动应用程序。方法：我们 将预处理和组织之前收集的猪多处烧伤数据 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）和计算机视觉作为指导评估任务。我们 将使用人工智能技术获得我们系统的每种不同输入模式的特征 将它们连接起来并训练 SVM 分类器来执行深度分类任务。然后，我们将使用一个 用于燃烧转换预测任务的异常检测方法。我们将测试其性能 该系统在一项用户研究中使用了更多患有多处烧伤的猪受试者。这样做的结果 研究将有助于援助从业人员和烧伤患者，改善烧伤的结果 即使烧伤专家不在场，也会造成伤害。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 强有力的预测可以为急诊医学提供新的医疗应用 治疗决策可以受益于强大的情报技术。