AI Medical Compendium Journal:
Cytometry. Part B, Clinical cytometry

Showing 1 to 7 of 7 articles

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Cytometry. Part B, Clinical cytometry
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...

MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.

Cytometry. Part B, Clinical cytometry
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...

Recommendations for using artificial intelligence in clinical flow cytometry.

Cytometry. Part B, Clinical cytometry
Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are trans...

Translating the regulatory landscape of medical devices to create fit-for-purpose artificial intelligence (AI) cytometry solutions.

Cytometry. Part B, Clinical cytometry
The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software produ...

Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence-assisted workflow.

Cytometry. Part B, Clinical cytometry
Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert...

Prediction of standard cell types and functional markers from textual descriptions of flow cytometry gating definitions using machine learning.

Cytometry. Part B, Clinical cytometry
BACKGROUND: A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non...

Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine-learning assisted morphometrics.

Cytometry. Part B, Clinical cytometry
BACKGROUND: The hallmark of myelodysplastic syndrome (MDS) remains dysplasia in the bone marrow (BM). However, diagnosing MDS may be challenging and subject to inter-observer variability. Thus, there is an unmet need for novel objective, standardized...