{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147526"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147526","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections","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_html":"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_has_math":false,"creators":["Wang, Lilian"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Fletcher, Richard R."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-09","date_published":"2022-09","updated_at":"2026-07-22T22:20:54Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/147526","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fletcher, Richard R."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Wang, Lilian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-01-19T19:56:12Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-01-19T19:56:12Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/147526"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fletcher, Richard R."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Wang, Lilian"],"dc:date.accessioned":["2023-01-19T19:56:12Z"],"dc:date.available":["2023-01-19T19:56:12Z"],"dc:date.issued":["2022-09"],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/147526"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:20:54Z"}