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

I.项目摘要：基于智能手机的伤口感染风险筛查和护理 利用深度学习方法结合热图和照片的推荐器 影响美国650万患者的慢性伤口严重影响了他们的生活质量，可能需要长达 60%-70%的患者需要一年的时间才能痊愈和复发。伤口经常被感染(伤口中的细菌)，导致四肢瘫痪 截肢，如果没有得到适当的治疗和时间1。在目前的实践中，在护理点(POC)(例如，护士 走访病人的家和创伤现场)，不是创伤专家的照顾者无法诊断 感染。因此，他们谨慎地将疑似感染的伤口送往诊所进行死亡组织的清创， 57-60岁的专家进行血液测试和感染诊断。然而，转诊增加了感染伤口之前的时间 治疗，以及截肢的机会。此外，一些被转移的伤口最终没有被感染，浪费了 病人和专家的时间和费用(例如交通费)15-16。我们需要的是一种数字健康解决方案 这使得非专业的伤口护理人员能够准确地检测POC的感染伤口，即使没有 并就循证护理和何时转诊提供标准化建议。 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 在美国，大多数伤口护理人员都拥有这种方法。GoYal等人之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到，例如伤口内/周围红度增加 在使用深度学习的照片中(准确度0.727±0.025，敏感度0.709±0.044，特异度0.744±0.05)。 尽管前景看好，但在临床应用之前，他们的结果还需要改进和验证。此外，他们的 数据集包括已经清创的伤口，感染病例很容易辨别，他们不建议 以证据为基础的最佳护理和决定何时转诊到伤口诊所是最佳行动方案。 某些热像模式是伤口感染的可靠指标，某些型号的智能手机 现在都配备了热像仪55。我们的假设是1)智能手机创伤的准确性 通过将热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)为可操作的、循证的伤口护理提供建议，以及何时 可以使用机器学习生成参考，以标准化非专家提供的护理。 为了回应NOT-EB-19-018，我们建议进行研究，以调查检测的能力和准确性 深度学习方法在伤口组合清创前感染创面的应用 照片和热成像，并产生护理和转介建议。我们还提议 将基于智能手机的感染筛查集成到我们集团现有的伤口评估系统7-9中， 21-29，并在新患者(N=100)上进行验证。我们提出的目标的成功将增加数量和 伤口感染的客观性在伤口诊所外检测并快速追踪到诊所进行治疗， 减少需要截肢的患者数量。我们的发现将适用于不同类型的伤口 包括糖尿病、血压、动脉、静脉、手术61和创伤62，它们都会被感染。

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.