The Attitudes and Perspectives of Laboratory Professionals on the Use of Machine Learning Combined with MALDI for Viral Identification: A Qualitative Study

Aug 5, 2026

Background: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) has been proposed for viral identification, yet laboratory professionals’ views on feasibility, accuracy, implementation, and impacts on existing workflows remain underexplored. Objective: Examine attitudes and opinions toward MALDI-TOF-MS coupled with ML for viral identification, including perceived benefits and barriers. Methods: Qualitative descriptive study using semi-structured interviews and coded categorization. Results: Identified attitudes, feasibility considerations, and implementation factors across standardization, training, cost, workflow, and implementation domains.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages54

Reading Time2 h 16 min

AccessFree to download

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Bowling Green State University ScholarWorks@BGSU | Honors Projects | Honors College | | --- | --- | | Spring 4-28-2025
The Attitudes and Perspectives of Laboratory Professionals on the Use of Machine Learning Combined with MALDI for Viral Identification: A Qualitative Study
Grace Johnson[gkjohns@bgsu.edu](gkjohns@bgsu.edu)
Follow this and additional works at: [https://scholarworks.bgsu.edu/honorsprojects](https://scholarworks.bgsu.edu/honorsprojects)
Part of the Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Laboratory Medicine Commons, and the Medical Microbiology Commons How does access to this work benefit you? Let us know! | | Repository Citation Johnson, Grace, "The Attitudes and Perspectives of Laboratory Professionals on the Use of Machine Learning Combined with MALDI for Viral Identification: A Qualitative Study" (2025) . Honors Projects. 1033. [https://scholarworks.bgsu.edu/honorsprojects/1033](https://scholarworks.bgsu.edu/honorsprojects/1033) This work is brought to you for free and open access by the Honors College at ScholarWorks@BGSU. It has been accepted for inclusion in Honors Projects by an authorized administrator of ScholarWorks@BGSU. The Attitudes and Perspectives of Laboratory Professionals on the Use of Machine Learning Combined with MALDI for Viral Identification: A Qualitative Study Grace Johnson Bowling Green State University HNRS 4990 Amanda Joost and Dr. Jake Lee April 21st, 2025 ABSTRACT Background: 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. Objective: 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. Methods: 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: 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. Conclusion: 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 v

FAQs

What is the research question of the study?

The study asks what attitudes and opinions clinical laboratory professionals have about applying a machine learning enhanced MALDI-TOF-MS system capable of testing for viruses.

What methods were used to gather and analyze data?

The study used a qualitative descriptive design with semi-structured interviews. Participants were recruited via convenience sampling and online private groups, and the data were analyzed through code categorization into aims, categories, and subcategories.

What key implementation factors were identified in the results?

The results identified factors including standardization/regulation, training, cost/funding, and workflow, along with considering/questioning feasibility and the domain/area of implementation.

Aug 5, 2026

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