AIMC Topic: Immunophenotyping
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Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF.
Frontiers in immunology
Oct 9, 2024
High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of Cy...
Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.
Frontiers in immunology
Jul 24, 2024
BACKGROUND: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP pat...
Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.
Frontiers in immunology
Jun 28, 2024
INTRODUCTION: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and person...
Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.
International journal of molecular sciences
Jun 12, 2024
Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecul...
Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.
Cytometry. Part B, Clinical cytometry
May 9, 2024
Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artifici...
Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.
Scientific reports
Apr 9, 2024
This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cyto...
Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.
Methods (San Diego, Calif.)
Mar 13, 2024
The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ...
MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.
Cytometry. Part B, Clinical cytometry
Feb 28, 2024
Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but...
Self-supervised deep learning for highly efficient spatial immunophenotyping.
EBioMedicine
Sep 4, 2023
BACKGROUND: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Labe...
Prostate Cancer: Early Detection and Assessing Clinical Risk Using Deep Machine Learning of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data.
Frontiers in immunology
Dec 16, 2021
Detecting the presence of prostate cancer (PCa) and distinguishing low- or intermediate-risk disease from high-risk disease early, and without the need for potentially unnecessary invasive biopsies remains a significant clinical challenge. The aim of...
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