A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images

Surg Infect (Larchmt). 2019 Oct;20(7):555-565. doi: 10.1089/sur.2019.154. Epub 2019 Aug 19.


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.

Keywords: surgical site infection; wound healing; wound management.

Publication types

  • Review

MeSH terms

  • Electronic Data Processing / methods*
  • Electronic Data Processing / trends
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Image Processing, Computer-Assisted / trends
  • Patient Generated Health Data*
  • Surgical Wound Infection / diagnostic imaging*
  • Telemedicine / methods*
  • Telemedicine / trends