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面临糖尿病足溃疡（DFU）的风险，其中超过10%的人被截肢。 当前的全球大流行推动了医疗保健服务的重大变化，扰乱了DFU护理， 肢体保留，使许多患者的临床护理有限或根本没有。 临床医生必须采取范式转变 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治疗的高风险病变。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，使用现场DFU对患者进行POC治疗。 用于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 不足以作为独立工具来评估和分诊高风险DFU病变，而无需现场伤口专业知识。 因此，额外的临床评估（例如，伤口供氧的程度）， 现场DFU。 氧合测量提供亚临床生理评估， 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），用于获得伤口中的可视组织氧合测量值。系统 皮肤色调和伤口特征的评估在生理成像期间是关键的， 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 (or智能扫描仪），能够跨肤色谱对DFU进行视觉和生理分析， 通过自动化机器学习（ML）算法来识别伤口特征。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，而是朝着智能健康平台的生理 DFU的评估，同时使用ML算法考虑不同的肤色和伤口类型， 新颖啊具体目标是：（i）通过以下方式说明肤色对氧合测量的影响： 应用光传播模型和机器学习算法，并通过体模和体内研究进行验证。 (ii)通过对照研究分析组织曲率并解释体内氧合图的深度变化 受试者（约15例）。(iii)使用SPOT区分伤口组织类型并验证生理成像 DFU研究中的器械（约25例）。预期成果是：（一）开发我们的智能扫描仪（SPOT 设备+应用程序）以获得跨不同肤色和伤口组织类型的准确组织氧合图; (ii)使用SPOT器械区分需要临床护理的高风险病变DFU和低风险病变DFU 例DFU的发生率和相关截肢率因人种/种族而异，非洲人更高 美国人，西班牙裔和美洲原住民与高加索人相比。从长远来看，SPOT可以作为 一种智能健康工具，用于对具有高风险病变的DFU进行预筛选或分类，以进行临床护理，从而最大限度地减少潜在的 任何种族/族裔群体的截肢（不同肤色）。