Honors Projects

Abstract

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research design was employed, utilizing semi-structured interviews with laboratory professionals. Participants were recruited through convenience sampling and through online private groups. Data was analyzed by categorizing codes under predetermined research aims and then further organized into categories and subcategories. Findings were quantified by reporting how many participants identified each category and subcategory, and participant quotes were included to provide deeper insights.

Results were organized as the Aim that the Category falls within, the Category, and respective Subcategories. Within Aim 1 two categories were identified: Attitudes (5 participants were coded as Positive and 1 as Negative) and Considering/Questioning Feasibility. Under Aim 2 four categories were identified: Standardization/Regulation, Training, Cost/Funding, and Workflow. Within Aim 3 only one category was identified: Domain/Area of Implementation. However, some additional trends of predictions were also identified within Aim 3. Each category also has respective subcategories identified.

This study identifies key attitudes and perspectives of lab professionals on implementing MALDI-TOF MS and machine learning in viral diagnostics. While this instrumentation holds potential, challenges related to development, adaptation or current workflow, regulation, and cost must be addressed. This study may be used to guide future research focusing on more expansive data collection methods and quantitative analysis to better understand the perspectives of lab professionals on the using of MALDI coupled with machine learning for viral diagnostics.

Department

Public and Allied Health

Major

Medical Laboratory Science

First Advisor

Amanda Joost

First Advisor Department

Public and Allied Health

Second Advisor

Dr. Jong Kwan Jake Lee

Second Advisor Department

Computer Science

Publication Date

Spring 4-28-2025

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