Smartphone-based optical scanner to physiologically assess diabetic foot ulcers

基于智能手机的光学扫描仪可对糖尿病足溃疡进行生理评估

基本信息

批准号：
10680499
负责人：
ANURADHA GODAVARTY
金额：
$ 51.77万
依托单位：
FLORIDA INTERNATIONAL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10680499
关键词：
3-Dimensional Adopted African American population Amputation Assessment tool Beer Caring Caucasians Cellular Phone Characteristics Chronic Chronic Care Clinical Clinical assessments Clinics and Hospitals Collaborations Communities Complement Complications of Diabetes Mellitus Development Devices Diabetes Mellitus Diabetic Foot Ulcer Discipline of Nursing Ethnic Origin Ethnic Population Excision Florida Functional Imaging Hispanic Americans Hospitalization Image Image Analysis Imaging technology Incidence International Laws Lesion Light Limb structure Maps Measurement Modeling Native Americans Near-infrared optical imaging Noise Optics Outcome Oxygen Patients Persons Physiological Pigmentation physiologic function Positioning Attribute Race Research Research Design Risk SMART health Signs and Symptoms Site Skin Surface Surgeon Technology Tissues Triage Validation Variant Visual clinical care computer science flu foot global health healing health care delivery high risk imaging approach in vivo innovation mHealth machine learning algorithm multidisciplinary mutant novel pandemic disease point of care preservation racial population skin color skin wound statistics tissue mapping tissue oxygenation tissue phantom tool validation studies wound wound care wound treatment

项目摘要

PROJECT SUMMARY One in three people with diabetes mellitus is at risk of diabetic foot ulcers (DFUs), with over 10% amputated. The current global pandemic has driven a significant change in healthcare delivery and disrupted DFU care and limb preservation, leaving many patients with limited or no clinical care. Clinicians must adopt a paradigm shift from the hospital and clinic care to community-based point-of-care (POC) - to best triage chronic DFU cases that are high-risk lesions requiring clinical care or hospitalization. There is an unmet clinical need for smart health assessment tools for POC treatment of patients with DFUs onsite, where no wound care expertise is available. Smartphone technologies for wound care are limited to 2D/3D wound image analysis for size/depth. They are insufficient as stand-alone tools to assess and triage high-risk DFU lesions without wound expertise onsite. Hence, additional clinical assessments (e.g., the extent of oxygen supply to wound) are required during POC of DFUs onsite. Oxygenation measurements provide a sub-clinical physiological assessment that complements clinical visual assessment. We recently developed a smartphone-based NIR imaging approach or SmartPhone Oxygenation Tool (SPOT) to obtain visual tissue oxygenation measurements in wounds. Systematic assessment of the skin tone and wound characteristics is critical during physiological imaging and has not been investigated to date. Hence, our objective is to develop and validate a smartphone-based imaging approach (or Smart Scanner) capable of visual and physiological analysis of DFUs across the spectrum of skin tones and wound features via automated machine learning (ML) algorithms. Developing a smartphone-based optical device via integration of existing NIR imaging technology, but towards smart health platform for physiological assessment of DFUs, while accounting for varying skin colors and wound types using ML algorithms is innovative. The specific aims are: (i) Account for the effect of skin tones on oxygenation measurements by applying light propagation models and machine learning algorithms and validate via phantom and in-vivo studies. (ii) Analyze tissue curvatures and account for depth variations in-vivo oxygenation maps via studies on control subjects (~15 cases). (iii) Differentiate wound tissue types and validate physiological imaging using the SPOT device via DFU studies (~25 cases). The expected outcomes are: (i) Develop our Smart Scanner (SPOT device + app) to obtain accurate tissue oxygenation maps across different skin tones and wound tissue types; (ii) Validate our SPOT device to differentiate DFUs with high-risk lesions that require clinical care, from low-risk cases. Incidence of DFUs and related amputation rates differ by race/ethnicity, and are higher in African Americans, Hispanic and Native Americans compared to Caucasians. In the long term, SPOT can be used as a smart health tool to pre-screen or triage DFUs with high-risk lesions to clinical care and thus minimize potential amputations in any racial/ethnic group (with varying skin tones).

项目概要三分之一的糖尿病患者有患糖尿病足溃疡 (DFU) 的风险，其中超过 10% 的人需要截肢。当前的全球大流行推动了医疗保健服务的重大变化，并扰乱了糖尿病足溃疡 (DFU) 护理和治疗肢体保留，使许多患者得到有限的临床护理或根本没有临床护理。临床医生必须转变范式从医院和诊所护理到社区护理点 (POC) - 对慢性 DFU 病例进行最佳分类是需要临床护理或住院治疗的高危病变。智能健康的临床需求尚未得到满足在没有伤口护理专业知识的情况下，用于对 DFU 患者进行 POC 现场治疗的评估工具。用于伤口护理的智能手机技术仅限于对伤口大小/深度进行 2D/3D 图像分析。他们是如果没有现场伤口专业知识，作为独立工具不足以评估和分类高风险 DFU 病变。因此，在 POC 期间需要进行额外的临床评估（例如，伤口的供氧程度） DFU 现场。氧合测量提供亚临床生理评估，补充临床视觉评估。我们最近开发了一种基于智能手机的近红外成像方法或智能手机氧合工具 (SPOT) 用于获得伤口组织氧合的可视化测量结果。系统化肤色和伤口特征的评估在生理成像过程中至关重要，但尚未得到证实调查至今。因此，我们的目标是开发和验证基于智能手机的成像方法（或智能扫描仪）能够对整个肤色范围内的 DFU 进行视觉和生理分析通过自动机器学习 (ML) 算法实现伤口特征。开发基于智能手机的光学设备通过整合现有的近红外成像技术，迈向生理智能健康平台评估 DFU，同时使用 ML 算法考虑不同的肤色和伤口类型创新的。具体目标是： (i) 通过以下方式解释肤色对氧合测量的影响：应用光传播模型和机器学习算法，并通过模型和体内研究进行验证。 (ii) 通过控制研究分析组织曲率并解释体内氧合图的深度变化受试者（~15 例）。 (iii) 使用 SPOT 区分伤口组织类型并验证生理成像通过 DFU 研究的设备（约 25 例）。预期成果是： (i) 开发我们的智能扫描仪（SPOT 设备+应用程序）以获得不同肤色和伤口组织类型的准确组织氧合图； (ii) 验证我们的 SPOT 设备，以区分需要临床护理的高风险病变 DFU 和低风险病变案例。 DFU 的发生率和相关截肢率因种族/民族而异，非洲地区较高美国人、西班牙裔和美洲原住民与白种人的比较。从长远来看，SPOT 可以用作一种智能健康工具，用于对具有高风险病变的 DFU 进行预筛查或分类以进行临床护理，从而最大限度地减少潜在风险任何种族/族裔群体（不同肤色）的截肢。