用智能拭子和手机App实时监测糖尿病足溃疡愈合情况
Multi-Analyte, Swab-based Automated Wound Monitor with AI
- 3D打印多指标拭子结合AI图像分析，自动检测伤口状态
- 通过前后图像密度变化判断伤口严重程度，准确率高
- 适合临床医生快速评估伤口，降低截肢风险
糖尿病足溃疡（DFUs）是慢性伤口的一种，仅在美国每年就影响约75万人。早期识别无法愈合的DFUs可显著降低治疗成本并减少截肢风险。为此，我们开发了一种低成本、多分析物的3D打印拭子传感器，可无缝集成于拭子上，并配套开发了Wound Sensor iOS App——一款用于规范采集与自动化分析伤口传感数据的移动应用。通过对比拭子暴露前后的基底图像与伤口暴露后图像，我们采用自动化计算机视觉技术分析两者间的密度变化，从而实现对伤口严重程度的自动判断。该App能克服相机配置差异和环境光变化等挑战，确保数据准确采集并提供可操作的洞察。该集成式传感器与App系统使医护人员能够实时监控伤口状况，追踪愈合进程，并评估关键护理参数。
原文摘要 · Abstract (English)
Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that develop to chronic wounds early can drastically reduce treatment costs and minimize risks of amputation. There is therefore a pressing need for diagnostic tools that can detect non-healing DFUs early. We develop a low cost, multi-analyte 3D printed assays seamlessly integrated on swabs that can identify non-healing DFUs and a Wound Sensor iOS App - an innovative mobile application developed for the controlled acquisition and automated analysis of wound sensor data. By comparing both the original base image (before exposure to the wound) and the wound-exposed image, we developed automated computer vision techniques to compare density changes between the two assay images, which allow us to automatically determine the severity of the wound. The iOS app ensures accurate data collection and presents actionable insights, despite challenges such as variations in camera configurations and ambient conditions. The proposed integrated sensor and iOS app will allow healthcare professionals to monitor wound conditions real-time, track healing progress, and assess critical parameters related to wound care.
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