Clinical Evaluation of Burns using Spatial Frequency Domain Imaging

使用空间频域成像对烧伤进行临床评估

• 批准号: 10224221
• 负责人: ANTHONY J DURKIN
• 金额: $ 44.11万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224221
• 关键词: Address; Animal Model; Area; Biometry; Blood Vessels; Burn Centers; Burn injury; Cicatrix; Classification; Clinic; Clinical; Clinical assessments; Collaborations; Custom; Data; Detection; Devices; Diagnosis; Diagnostic; Enrollment; Eye; Family suidae; Female; Hemoglobin; Hospital Costs; Hospitalization; Hour; Hybrids; Image; Imaging Device; Injury; Laser Speckle Imaging; Lasers; Light; Lighting; Machine Learning; Maps; Measurement; Measures; Medical center; Methods; Modality; Modeling; Morphologic artifacts; Motion; Noise; Optics; Output; Patients; Physicians; Principal Investigator; Property; Reporting; Research; Severities; Side; Signal Transduction; Skin; Spatial Frequency Domain Imaging; System; Techniques; Testing; Thick; Time; Tissues; Training; Translating; Ulcer; Variant; Visual; Water; Work; base; burden of illness; burn model; burn wound; care costs; clinical imaging; cost; data acquisition; data integrity; data modeling; diverse data; experience; healing; human data; imaging system; impression; improved; in vivo; infection rate; male; next generation; novel; optical imaging; porcine model; pre-clinical; programs; research clinical testing; second degree burn; stability testing; third degree burn; treatment planning

Program Director/Principal Investigator (Last, First, Middle): Durkin, Anthony J. Abstract The central aim of this 3 year competing R01 renewal is to characterize and apply a new, compact, clinic- friendly Spatial Frequency Domain Imaging (SFDI) device to objectively and non-invasively classify burn severity (burn grade) over a large areas of skin. Delays in determining burn severity directly impacts patient treatment plans (including decisions whether to graft), rates of infection and scarring, duration of hospitalization and ultimately cost of care. Currently, the primary method of determining burn severity continues to be clinical assessment, which is highly subjective. While both superficial thickness and full-thickness burns are typically readily diagnosed based on visual clinical impression, partial thickness burns are difficult to classify and carry with them considerable potential for complications. Burn severity classification accuracy, even by experts, is only 60–80%. Our research in animal models demonstrates that SFDI data can successfully be used to classify different regions of burn severities. Typically, these differences are not apparent to the unaided eye and a great deal of training and experience is required in order for clinicians to accurately differentiate them Our work using a research grade, hybrid-SFDI device suggests that objective parameters provided by SFDI can be used within 24 hours after injury, to accurately classify burn severity. Specifically, we have demonstrated in a porcine burn model that the research grade SFDI outperforms laser speckle imaging and thermal imaging at 24 hours post-burn, in terms of predicting whether a burn will require a graft or not. However, translating these results to the clinic has been difficult due to several device limitations. The research grade SFDI device has slow acquisition times that can result in motion artifacts. It is also sensitive to ambient light which is often an issue in a clinical setting. Additionally, the SFDI device generates so much diverse data (oxygenated and deoxygenated hemoglobin, water fraction, reduced scattering coefficients at multiple wavelengths), there is no obvious way to present it to a clinical user to make a quick decision. To this end, we propose to methodically investigate an improved next generation SFDI device that addresses these issues by using brighter LEDs and fewer wavelengths to rapidly collect data in a way that reduces motion artifacts and is independent of clinical lighting conditions. In addition, we will develop a machine learning based classification framework that will provide the clinical with actionalble diagnostic information. The central aim of this 3 year competing R01 renewal is to characterize and then modify a new clinic-friendly SFDI device (Clarifi) to objectively classify in- vivo regions of different burn severity over large areas. The proposed research seeks to investigate this via the following Specific Aims: 1) Test & Validate Clinical SFDI Instrument, 2) Compare Clinical SFDI Instrument to other Modalities on a Long Term Swine Model of Graded Burns, 3) Develop Spatially Resolved Classification Maps of Burn Severity based on SFDI Data, 4) Conduct Clinical Measurements of Burn Severity using the new SFDI device and Spatially Resolved Burn Severity Classification Maps based on SFDI data.

项目总监/首席研究员（最后、第一、中间）：Durkin, Anthony J. 抽象的 R01 更新为期 3 年的竞争性更新的中心目标是描述和应用一种新的、紧凑的临床- 友好的空间频域成像 (SFDI) 设备，可客观、非侵入性地对烧伤进行分类 大面积皮肤的严重程度（烧伤等级）。延迟确定烧伤严重程度直接影响患者 治疗计划（包括是否移植的决定）、感染率和疤痕形成率、住院时间 以及最终的护理费用。目前，确定烧伤严重程度的主要方法仍然是临床方法 评价，主观性很强。虽然浅层烧伤和全层烧伤通常都是 根据视觉临床印象很容易诊断，部分厚度烧伤难以分类和携带 他们有相当大的潜在并发症。烧伤严重程度分类的准确性，即使是专家的，也是 只有 60-80%。我们对动物模型的研究表明，SFDI 数据可以成功地用于分类 烧伤严重程度不同的区域。通常，这些差异对于肉眼来说并不明显，并且 临床医生需要大量的培训和经验才能准确地区分它们 我们的工作 使用研究级混合 SFDI 设备表明可以使用 SFDI 提供的客观参数 受伤后 24 小时内，准确分类烧伤严重程度。具体来说，我们已经在 猪烧伤模型表明，研究级 SFDI 在 24 ℃ 的性能优于激光散斑成像和热成像 烧伤后数小时，以预测烧伤是否需要移植。然而，翻译这些 由于多种设备的限制，临床结果一直很困难。研究级 SFDI 设备具有 采集时间慢，可能会导致运动伪影。它对环境光也很敏感，这通常是 临床环境中的问题。此外，SFDI 设备还生成如此多的不同数据（含氧和含氧数据） 脱氧血红蛋白、水分数、多个波长下的散射系数降低），没有 将其呈现给临床用户以做出快速决定的明显方式。为此，我们建议有条不紊地 研究改进的下一代 SFDI 设备，该设备通过使用更亮的 LED 来解决这些问题 更少的波长来快速收集数据，从而减少运动伪影并且独立于临床 照明条件。此外，我们将开发一个基于机器学习的分类框架，该框架将 为临床提供可操作的诊断信息。 R01这三年竞赛的中心目标 更新是表征并修改一种新的临床友好型 SFDI 设备 (Clarifi)，以客观地对 in- 大面积不同烧伤严重程度的体内区域。拟议的研究旨在通过 以下具体目标：1) 测试和验证临床 SFDI 仪器，2) 将临床 SFDI 仪器与 猪分级烧伤长期模型的其他模式，3) 开发空间分辨分类 基于 SFDI 数据的烧伤严重程度图，4) 使用新的方法进行烧伤严重程度的临床测量 SFDI 设备和基于 SFDI 数据的空间解析烧伤严重程度分类图。