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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

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Free PMC article
Review

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

Ziyu Jiang et al. Surg Infect (Larchmt). .
Free PMC article

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.

Conflict of interest statement

All authors report no competing financial interests exist.

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References

    1. Gunter R, Chouinard S, Fernandes-Taylor S, et al. Current use of telemedicine for post-discharge surgical care: A systematic review. Am Coll Surg 2016;222:915–927 - PMC - PubMed
    1. Zhao R, Grosky WI. Narrowing the semantic gap—Improved text-based web document retrieval using visual features. IEEE Trans Multimedia 2002;4:189–200
    1. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Bartlett P, editor; , Pereira F, editor; , Burges CJC, et al. (eds): Advances in Neural Information Processing Systems 25. (NIPS 2012). 2012:1097–1105
    1. Wang Z, Chang S, Yang Y, et al. Studying very low resolution recognition using deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016:4792–4800
    1. Liu D, Cheng B, Wang Z, et al. Enhance visual recognition under adverse conditions via deep networks. IEEE Trans Image Proc (in press) - PubMed

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