I-Corps: Machine Learning-Based Burn Injury Diagnosis and Care

I-Corps：基于机器学习的烧伤诊断和护理

• 批准号: 2326781
• 负责人: Alan Pang
• 金额: $ 5万
• 依托单位: Texas Tech University Health Science Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326781&HistoricalAwards=false
• 关键词: Corps; Machine; Learning; Based; Burn

The broader impact/commercial potential of this I-Corps project is the development of machine learning technology to evaluate patients with skin burns. The proposed software tool is designed to determine burn severities and surface area percentages quickly and accurately. Currently, medical professionals use a visualized method to look at wounds and determine burn severities and to estimate burn percentage versus unburned skin. This may result in a wait of 3-7 days during which time doctors determine the burn degree by comparing pictures from the first day and following days of continuous monitoring. The proposed image processing and machine learning algorithm may provide clinicians with the ability to predict total burn surface area, expected resuscitation amount, delineate second- and third-degree burns, as well as prognosticate indeterminate-degree burns. The proposed technology may reduce ICU cost for both patients and hospitals, and may allow for better, more efficient patient care, better survival, and better functional outcomes.This I-Corps project is based on the development of a machine learning algorithm to process imaging from burn patients. The proposed assist tool is designed to determine the total body surface area of burns using an image capture technology. Image capture technology may be used to calculate total body surface area based on image recognition of burned areas, providing physicians and other medical personnel with more objective data on which to base resuscitation. In addition, the proposed technology may be used to delineate between full thickness and partial thickness of burn injuries. Also, it may be used to delineate between operative and non-operative indeterminate thickness burn injuries. Burn wounds are always evolving, and based on the depth of a burn injury, an operation may be required. The lengthy observation period with current visual methods exposes patients to infection, hospital-related comorbidity, and life-altering hospital cost. Using image capture technology and machine learning to analyze a standardized image of burn injuries to delineate between operative and non-operative burns may mitigate these risks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目更广泛的影响/商业潜力是开发机器学习技术来评估皮肤烧伤患者。所提出的软件工具旨在快速准确地确定烧伤严重程度和表面积百分比。 目前，医疗专业人员使用可视化方法来观察伤口并确定烧伤严重程度，并估计烧伤百分比与未烧伤皮肤。 这可能导致等待3-7天，在此期间，医生通过比较连续监测的第一天和随后几天的照片来确定烧伤程度。 所提出的图像处理和机器学习算法可以为临床医生提供预测总烧伤表面积、预期复苏量、描绘二度和三度烧伤以及不确定程度烧伤的能力。 所提出的技术可以降低ICU成本为患者和医院，并可能允许更好，更有效的病人护理，更好的生存，更好的功能outcomes.This I-Corps项目是基于机器学习算法的开发来处理烧伤患者的成像。 所提出的辅助工具被设计成使用图像捕获技术来确定烧伤的总体表面积。 图像捕获技术可用于基于烧伤区域的图像识别来计算总体表面积，从而为医生和其他医务人员提供更客观的数据以作为复苏的基础。 此外，所提出的技术可用于在全厚度和部分厚度烧伤之间划定界限。 此外，它可以用来划定手术和非手术不确定厚度烧伤。烧伤伤口总是在不断发展，根据烧伤的深度，可能需要进行手术。目前的视觉方法的观察期过长，使患者暴露于感染、医院相关并发症和改变生活的医院费用。使用图像捕获技术和机器学习来分析烧伤的标准化图像，以区分手术和非手术烧伤可能会降低这些风险。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。