A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images
A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images
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DOI：
10.1089/sur.2019.154
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发表时间：
2019-08-19
影响因子：
2
通讯作者：
Huang, Shuai
中科院分区：
文献类型：
--
作者：
Jiang, Ziyu;Ardywibowo, Randy;Huang, Shuai
背景资料：智能手机和可穿戴传感器等新兴技术以及最近的移动的健康（mHealth）应用程序开发，使模式转变为以患者为中心的新医疗保健。一个这样有前途的医疗保健应用程序是基于患者拍摄的切口图像的切口监测。在这篇综述中，挑战和潜在的解决方案策略进行调查，手术部位感染（SSI）的检测和评价，使用手术部位的图像在家里。方法：讨论了潜在的图像质量问题、特征提取和手术部位图像分析挑战。回顾了最近的图像分析和机器学习解决方案，以提取有意义的表示作为切口监测的图像标记。讨论的机会和挑战，应用这些方法来获得准确的SSI预测。结论：交互式图像采集以及用于SSI监测的定制图像分析和机器学习方法将在开发可持续的移动健康应用程序方面发挥关键作用，以实现患者拍摄的切口图像的预期结果，从而实现以患者为中心的有效门诊医疗保健，同时大幅降低成本。
Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.