Smartphone-based wound infection screener and care recommender by combining thermal images and photographs using deep learning methods

使用深度学习方法结合热图像和照片，基于智能手机的伤口感染筛查和护理推荐

• 批准号: 10442952
• 负责人: Emmanuel Agu
• 金额: $ 66.02万
• 依托单位: WORCESTER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10442952
• 关键词: Accident and Emergency department; Affect; Agreement; Algorithms; Amputation; Appointment; Bacteria; Blood Tests; Caregivers; Caring; Cellular Phone; Clinic; Data Set; Debridement; Detection; Diabetic Foot; Diabetic Foot Ulcer; Diagnosis; Emergency Care; Emergency medical service; Excision; Home; Home visitation; Hospitals; Image; Image Analysis; Infection; Lead; Lighting; Machine Learning; Manuals; Measures; Medicare; Methods; Modeling; Operative Surgical Procedures; Patients; Pattern; Performance; Persons; Positioning Attribute; Process; Quality of Care; Quality of life; Recommendation; Redness; Reporting; Research; Resolution; Running; Sensitivity and Specificity; Sepsis; Services; Site; Specificity; Standardization; System; Temperature; Testing; Thermometers; Time; Tissues; Transportation; Trauma; Ulcer; Venous; Visit; Visiting Nurse; Visual; Work; Wound Infection; accurate diagnosis; base; beneficiary; chronic wound; clinical application; clinical decision support; cost; deep learning; detector; diabetic; digital health; evidence base; healing; improved; infection risk; learning strategy; limb amputation; machine learning algorithm; novel; point of care; pressure; response; smartphone Application; standardized care; success; validation studies; wasting; wound; wound care; wound healing; wound treatment

I. PROJECT SUMMARY: Smartphone-based wound infection risk screener and care recommender by combining thermal images and photographs using deep learning methods Chronic wounds, which affect 6.5 million patients in the US12 severely affect their quality of life, can take up to a year to heal and re-occur in 60-70% of patients. Wounds often get infected (bacteria in wound), resulting in limb amputations if not treated properly and on time1. In current practice, at the Point of Care (POC) (e.g., nurses visiting patients’ homes and trauma sites), caregivers who are not wound experts have no way to diagnose infections. Thus, they cautiously refer wounds suspected to be infected to clinics for debridement of dead tissues, blood tests and infection diagnoses by experts57-60. However, referrals increase time before infected wounds are treated, and the chances of limb amputation. Moreover, some referred wounds end up not being infected, wasting patient and expert time and expenses (e.g., transportation)15-16. What is needed is a digital health solution that enables non-expert wound caregivers to accurately detect infected wounds at the POC even without debridement and provide standardized recommendations on evidence-based care and when to refer. Smartphones equipped with high resolution cameras and the processing power to run machine/deep learning methods are owned by most wound caregivers in the US56. Prior work by Goyal et al1 reported preliminary results that show that infection can be detected from visual attributes such as increased redness in/around the wound in a photograph using deep learning (accuracy 0.727± 0.025, sensitivity 0.709 ± 0.044, specificity 0.744 ± 0.05). While promising, their results need to be improved and validated before clinical applications. Moreover, their dataset included already debrided wounds with easily discernable infection cases, and they did not recommend evidence based best care and decide when referrals to wound clinics were the best course of action. Certain thermal image patterns are reliable indicators of wound infection20, and some models of smartphones are now equipped with thermal cameras55. Our hypotheses are that 1) the accuracy of smartphone wound infection detection can be improved by combining thermal images with photographs jointly analyzed using a deep learning method 2) recommendations for actionable, evidence-based wound care and when to refer can be generated using machine learning to standardize care provided by non-experts. In response to NOT-EB-19-018, we propose research to investigate the capability and accuracy of detecting infected wounds before debridement using deep learning methods applied to combinations of wound photographs and thermal images and generating care and referral recommendations. We also propose integration of the smartphone-based infection screener into our group’s existing wound assessment system7-9, 21-29 and validating it on new patients (N=100). Success on our proposed aims will increase the number and objectivity of wound infections detected outside the wound clinic and fast-tracked to the clinic for treatment, reducing the number of patients who require amputations. Our findings will apply to diverse wound types including diabetic, pressure, arterial, venous, surgical61 and trauma wounds62, which all get infected.

I. 项目摘要：基于智能手机的伤口感染风险筛查和护理 使用深度学习方法结合热图像和照片进行推荐 慢性伤口影响着美国 650 万名患者12，严重影响他们的生活质量，可能需要长达 60-70% 的患者伤口通常需要一年才能愈合并再次发生（伤口内有细菌），导致肢体损伤。 如果治疗不当，可能会导致截肢1 在目前的实践中，在护理点 (POC)（例如护士）。 探访患者家中和创伤部位），非伤口专家的护理人员无法诊断 因此，他们谨慎地将疑似感染的伤口转诊至诊所进行坏死组织清创， 由专家进行血液检查和感染诊断57-60 然而，转诊会增加感染伤口出现之前的时间。 此外，一些涉及的伤口最终不会被感染，浪费。 患者和专家的时间和费用（例如交通）15-16。 使非专业伤口护理人员能够在 POC 中准确检测感染伤口，即使没有 清创术并提供有关循证护理和何时转诊的标准化建议。 配备高分辨率摄像头和运行机器/深度学习的处理能力的智能手机 Goyal 等人之前的工作报告了初步结果，美国大多数伤口护理人员都拥有这种方法。 这表明可以通过视觉属性来检测感染，例如伤口内/周围的发红程度增加 使用深度学习的照片（准确度 0.727± 0.025，灵敏度 0.709 ± 0.044，特异性 0.744 ± 0.05）。 虽然很有希望，但他们的结果在临床应用之前需要改进和验证。 数据集包括已经清创的伤口和容易辨别的感染病例，他们不推荐 基于证据的最佳护理并决定何时转诊至伤口诊所是最佳行动方案。 某些热图像图案是伤口感染的可靠指标20，并且某些型号的智能手机 现在配备了热感摄像头55。我们的假设是 1) 智能手机缠绕的准确性。 通过将热图像与联合分析的照片相结合，可以改进感染检测 使用深度学习方法 2) 针对可操作、基于证据的伤口护理的建议以及何时 可以使用机器学习生成参考信息，以标准化非专家提供的护理。 为了回应 NOT-EB-19-018，我们提出研究来调查检测的能力和准确性 使用应用于伤口组合的深度学习方法在清创前感染伤口 照片和热图像并生成护理和转诊建议。 将基于智能手机的感染筛查仪集成到我们集团现有的伤口评估系统中7-9， 21-29 并在新患者上进行验证（N=100）。我们提出的目标的成功将增加数量和数量。 在伤口诊所外检测到伤口感染并快速送往诊所治疗的客观性， 减少需要截肢的患者数量我们的研究结果将适用于不同的伤口类型。 包括糖尿病伤口、压力伤口、动脉伤口、静脉伤口、手术伤口61和外伤伤口62，这些伤口都会被感染。