BACTERIA ANALYSIS BY USING A SUPERVISED MACHINE LEARNING ALGORITHM BASED ON DROPLET MICROFLUIDICS

<p>Sepsis is a major medical problem and massive resources have been invested in developing and evaluating alternative treatments. Statistics indicate that sepsis causes between one third and one half of all hospital deaths in the United States. Sepsis has a high impact on health care in the US, with direct sepsis costs in 2009 exceeding $15.4 billion. A research study found that a 1-hour delay in appropriate antimicrobial care resulted in a 7% - 10% rise in mortality. Several professional societies seek to reduce sepsis mortality by targeting the timely use of diagnostic tests and antimicrobial therapy. The diagnostic instruments available to clinicians to identify the suspected pathogen do not make a timely intervention possible. Up to 5 days of incubation are needed for blood cultures, the majority of bacteria being detected after 12–48 h. Therefore, fast and simple techniques are required for rapid bacterial cell detection and quantification. By using droplet microfluidics and a machine learning algorithm, the objective of this study was to propose a technology that analyzes images of bacterial cells by image processing and Support Vector Machines algorithm to classify droplets containing the bacteria. The accuracy of the proposed technology was 97.2 % for a trained SVM model and with the complete identification and classification of droplets.</p>

Name thesis:degree_name

MS

Level thesis:degree_level

One-year Embargo

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2020

Author dc:creator

• Angarita, Yulder Daniel

Contributors dc:contributor

• Melikhan Tanyeri
• Kimberly Forsten Williams

• Droplet Microfluidics
• Machine Learning
• Support Vector Machines
• E. Coli
• Bacteria Quantification and Detection
• Droplet Classification
• Intensity Based Algorithm
• Bacteria Images
• Bioimaging and Biomedical Optics
• Biological Engineering
• Biomedical Devices and Instrumentation
• Molecular, Cellular, and Tissue Engineering

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/1905

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-2929