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Fletcher, Richard R. Wang, Lilian Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2023-01-19T19:56:12Z 2023-01-19T19:56:12Z 2022-09 2022-09-16T20:24:20.392Z https://hdl.handle.net/1721.1/147526 The infection of surgical wounds, also known as surgical site infections (SSI), represents a significant financial cost for health care systems worldwide but also represents a threat to the life and health of women in developing countries who give birth by Cesarean section (C-section). In order to help monitor and detect SSI in women who recently underwent C-section births, this thesis presents the design and development of an integrated smartphone application that can be used by community health workers (CHW) to help detect SSI using a smartphone camera image. This mobile application includes four main components: (1) a computer vision image capture algorithm with automated image scaling, cropping and rotation; (2) automated image quality assessment to provide real-time feedback to the CHW; (3) an image processing pipeline to perform image sampling, color correction, and brightness adjustment; (4) integrated image-based machine learning prediction, making use of a previouslydeveloped convolutional neural network (CNN) model. The integrated smartphone application, created with the Android Java SDK, is primarily designed to operate in rural parts of the world where there is a lack of Internet access. However, the mobile application is also designed to connect and synchronize data with a remote electronic medical record (EMR) server developed at MIT, known as the PyMed EMR server. In this thesis, I describe the design and implementation of the main components of the mobile application and the complete application work flow. I also discuss the performance of the application on different mobile phone models as well as the performance trade-off between online and offline wound infection prediction. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="176f9851-ff37-41eb-b725-28b195963cd3"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-09</PublicationDate> <Authors> <Author> <DisplayName>Wang, Lilian</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>The infection of surgical wounds, also known as surgical site infections (SSI), represents a significant financial cost for health care systems worldwide but also represents a threat to the life and health of women in developing countries who give birth by Cesarean section (C-section). In order to help monitor and detect SSI in women who recently underwent C-section births, this thesis presents the design and development of an integrated smartphone application that can be used by community health workers (CHW) to help detect SSI using a smartphone camera image. This mobile application includes four main components: (1) a computer vision image capture algorithm with automated image scaling, cropping and rotation; (2) automated image quality assessment to provide real-time feedback to the CHW; (3) an image processing pipeline to perform image sampling, color correction, and brightness adjustment; (4) integrated image-based machine learning prediction, making use of a previouslydeveloped convolutional neural network (CNN) model. The integrated smartphone application, created with the Android Java SDK, is primarily designed to operate in rural parts of the world where there is a lack of Internet access. However, the mobile application is also designed to connect and synchronize data with a remote electronic medical record (EMR) server developed at MIT, known as the PyMed EMR server. In this thesis, I describe the design and implementation of the main components of the mobile application and the complete application work flow. I also discuss the performance of the application on different mobile phone models as well as the performance trade-off between online and offline wound infection prediction.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>