Surgical Wounds Assessment System for Self-Care
Journal
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Journal Volume
50
Journal Issue
12
Pages
5076-5091
Date Issued
2020
Author(s)
Abstract
The importance of effective surgical wound care cannot never be underestimated. Poorly managing surgical wounds may cause many serious complications. Thus, it raises the necessity to develop a patient-friendly self-care system which can help both patients and medical professionals to ensure the state of the surgical wounds without any special medical equipment. In this paper, a surgical wound assessment system for self-care is proposed. The proposed system is designed to enable patients capture surgical wound images of themselves by using a mobile device and upload these images for analysis. Combining image-processing and machine-learning techniques, the proposed method is composed of four phases. First, images are segmented into superpixels where each superpixel contains the pixels in the similar color distribution. Second, these superpixels corresponding to the skin are identified and the area of connected skin superpixels is derived. Third, surgical wounds will be extracted from this area based on the observation of the texture difference between skin and wounds. Lastly, state and symptoms of surgical wound will be assessed. Extensive experimental results are conducted. With the proposed method, more than 90% state assessment results are correct and more than 91% symptom assessment results consistent with the actual diagnosis. Moreover, case studies are provided to show the advantage and limitation of this system. These results show that this system could perform well in the practical self-care scenario. © 2013 IEEE.
Subjects
Artificial intelligence (AI); classification; health care service systems
SDGs
Other Subjects
Artificial intelligence; Cameras; Classification (of information); Color image processing; Diagnosis; Feature extraction; Image analysis; Skin; Superpixels; Surgery; Surgical equipment; Textures; Assessment system; Biomedical monitoring; Color distribution; Healthcare services; Image color analysis; Machine learning techniques; Medical professionals; Wounds; Learning systems
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
journal article