Integrated Smartphone-Based Computer Vision and Machine Learning Platform for Identification of Surgical Site Infections
Name
Wang-lilianw-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
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11.49 MB
Format
Adobe PDF
Checksum (MD5)
c2ede1e29dcfa37d265561d52bc41ca1
Author(s)
Wang, Lilian
Advisor(s)
Fletcher, Richard R.
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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