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

• 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
• 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

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).

项目摘要 三分之一的糖尿病患者有糖尿病足溃疡（DFU）的风险，截肢超过10％。 当前的全球大流行导致医疗保健提供和DFU护理中断的重大变化和 肢体保存，使许多患者拥有有限或没有临床护理。 临床医生必须采用范式转变 从医院和诊所护理到基于社区的护理点（POC） - 最好的分类慢性DFU病例 是需要临床护理或住院的高风险病变。对智能健康有未满足的临床需求 用于POC治疗DFUS现场患者的评估工具，那里没有伤口护理专业知识。 伤口护理的智能手机技术仅限于尺寸/深度的2D/3D伤口图像分析。他们是 作为独立工具不足以评估和分类高危DFU病变，而没有伤口专业知识。 因此，在POC期间需要进行其他临床评估（例如，伤口供应的程度） 现场。 氧合测量提供了一个亚临床生理评估，以补充 临床视觉评估。我们最近开发了一种基于智能手机的NIR成像方法或智能手机 氧合工具（斑点）以获得伤口中的视觉组织氧合测量。系统 在生理成像期间，评估肤色和伤口特征至关重要，尚未 迄今已调查。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）能够对肤色范围的DFU进行视觉和生理分析和生理分析 通过自动化机器学习（ML）算法的伤口功能。开发基于智能手机的光学设备 通过集成现有的NIR成像技术，但要朝着生理的智能健康平台 评估DFU，同时使用ML算法考虑了不同的肤色和伤口类型的是 创新的。具体目的是：（i）通过通过 应用光传播模型和机器学习算法并通过幻影和体内研究验证。 （ii）分析组织曲率并通过对照研究来解释深度变化体内氧合图 受试者（约15例）。 （iii）使用该点区分伤口组织类型并验证生理成像 通过DFU研究（约25例）设备。预期的结果是：（i）开发我们的智能扫描仪（斑点 设备 + App）以获取在不同的肤色和伤口组织类型上获得准确的组织氧合图； （ii）验证我们的现场装置，以使用需要临床护理的高风险病变来区分DFU，从 案例。 DFU和相关截肢率的发生率因种族/种族而异，非洲的发病率较高 与高加索人相比，美国人，西班牙裔和美洲原住民。从长远来看，点可以用作 一种智能健康工具，用于预屏幕或分类DFU，具有高风险病变以临床护理，从而最大程度地降低潜力 任何种族/种族的截肢（带有不同的肤色）。