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.项目总结：基于智能手机的伤口感染风险筛查和护理 通过使用深度学习方法组合热图像和照片的推荐器 在美国，慢性伤口影响650万患者12，严重影响他们的生活质量， 一年内治愈，60-70%的患者复发。伤口经常感染（伤口中的细菌），导致肢体 如果没有及时得到适当的治疗，可能会导致截肢1。在目前的实践中，在护理点（POC）（例如，护士 访问病人的家和创伤部位），不是伤口专家的护理人员无法诊断 感染.因此，他们谨慎地将怀疑感染的伤口转介到诊所进行死亡组织清创， 专家进行血液检验和感染诊断57 -60。然而，转诊增加了感染伤口被治愈之前的时间。 治疗和截肢的机会。此外，一些转诊的伤口最终没有被感染， 患者和专家的时间和费用（例如，运输）15-16.我们需要的是数字健康解决方案 这使得非专业的伤口护理人员能够准确地检测POC处的感染伤口， 清创术，并提供标准化的循证护理建议和何时转诊。 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 在美国，大多数伤口护理人员都拥有这些方法56。Goyal等人1先前的工作报告了初步结果 这表明感染可以从视觉属性中检测出来 在使用深度学习的照片中（准确度0.727± 0.025，灵敏度0.709 ± 0.044，特异性0.744 ± 0.05）。 虽然有希望，但在临床应用之前，它们的结果需要改进和验证。而且他们的 数据集包括已经清创的伤口，感染病例很容易识别，他们不建议 以证据为基础的最佳护理，并决定何时转诊到伤口诊所是最好的行动方案。 某些热图像模式是伤口感染的可靠指标20，某些型号的智能手机 现在配备了热成像摄像机55。我们的假设是：1）智能手机伤口的准确性 通过将热图像与照片结合起来进行联合分析，可以改进感染检测 使用深度学习方法2）可操作的循证伤口护理建议， 可以使用机器学习来生成转介，以标准化由非专家提供的护理。 作为对NOT-EB-19-018的回应，我们提出了研究，以调查检测的能力和准确性 在清创术之前使用应用于伤口组合的深度学习方法的感染伤口 照片和热成像，并生成护理和转诊建议。我们亦建议 将基于智能手机的感染筛选器整合到我们小组现有的伤口评估系统中7 -9， 21-29，并在新患者（N=100）上验证。我们提出的目标的成功将增加数量， 在伤口诊所外检测到伤口感染并快速跟踪到诊所进行治疗的客观性， 减少需要截肢的病人数量。我们的发现将适用于不同的伤口类型 包括糖尿病、压力、动脉、静脉、创伤61和创伤62，这些伤口都会感染。