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

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

Ziyu Jiang  1 , Randy Ardywibowo  2 , Aven Samereh  3 , Heather L Evans  4 , William B Lober  5 , Xiangyu Chang  3   6 , Xiaoning Qian  2 , Zhangyang Wang  1 , Shuai Huang  3
Affiliations
Review

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

Ziyu Jiang et al. Surg Infect (Larchmt). 2019 Oct.

Abstract

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.

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Conflict of interest statement

All authors report no competing financial interests exist.

Figures

<b>FIG. 1.</b>
FIG. 1.
Example of the challenges faced characterizing surgical site images, including poor lighting conditions, obstructing objects such as hair, stitches, and thin films, as well as different camera angles and positioning. Color image is available online.
<b>FIG 2.</b>
FIG 2.
The architecture of WoundSeg [63]. Color image is available online.
<b>FIG 3.</b>
FIG 3.
(a) Summer picture and (b) corresponding winter picture while F and H are translation functions. (b) Consistency loss measures the distance between a and a 0, in which a 0 = H(F(a)). Color image is available online.
<b>FIG 4.</b>
FIG 4.
The domain translation between summer and winter pictures [95]. Color image is available online.
<b>FIG 5.</b>
FIG 5.
Interactive procedure for image capture using mobile phones. Color image is available online.
<b>FIG 6.</b>
FIG 6.
Envisioned interactive image capture system. Color image is available online.
<b>FIG 7.</b>
FIG 7.
Deep image segmentation with noisy labels: (left) synthetic testing image example; (middle) simulated noisy segmented image; (right) image segmentation by modified U-net using the simulated noisy segmentation. Color image is available online.
See this image and copyright information in PMC

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