Laboratory Medicine Commons^™

Machine Learning In Flow Cytometry For Acute Myeloid Leukemia Diagnosis: A Scoping Review, Katrina Jezzela M. Dela Pena Md, Mls (Ascp)

Other Specialties

Manuscript version:

Introduction: Flow cytometry is essential for the diagnosis of acute myeloid leukemia (AML), but conventional analysis is labor-intensive, operator-dependent, and increasingly complex. Machine learning (ML) may support automated analysis; however, its readiness for clinical implementation remains unclear. This scoping review mapped ML approaches applied to flow cytometry for AML-related diagnosis.

Methods: PubMed was searched for English-language primary studies applying artificial intelligence, ML, or deep learning to human flow cytometry data for AML diagnosis or classification. Eligible studies reported diagnostic performance metrics. Study characteristics, preprocessing, data representation, model architecture, validation strategy, performance metrics, interpretability, and implementation considerations were descriptively …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

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 …

Effects Of Image Quantity And Image Source Variation On Machine Learning Histology Differential Diagnosis Models, Elham Vali-Betts, Kevin J Krause, Alanna Dubrovsky, Kristin Olson, John Paul Graff, Anupam Mitra, Ananya Datta-Mitra, Kenneth Beck, Aristotelis Tsirigos, Cynthia Loomis, Antonio Galvao Neto, Esther Adler, Hooman H Rashidi

Faculty, Staff and Student Publications

Aims:

Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of …