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Myelodysplastic Syndromes

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A machine learning approach to predicting risk of myelodysplastic syndrome.

Leukemia research
BACKGROUND: Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from othe...

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes.

Scientific reports
Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to r...

Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.

Scientific reports
Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and effort. ...

Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears.

Clinical chemistry and laboratory medicine
OBJECTIVES: VEXAS syndrome is a newly described autoinflammatory disease associated with somatic mutations and vacuolization of myeloid precursors. This disease possesses an increasingly broad spectrum, leading to an increase in the number of suspec...

Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry.

Haematologica
The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular factors. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features do not con...

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.

Scientific reports
Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standa...

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

JCO clinical cancer informatics
PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innov...

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Medicine
Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patie...

Deep Learning-Based Blood Abnormalities Detection as a Tool for VEXAS Syndrome Screening.

International journal of laboratory hematology
INTRODUCTION: VEXAS is a syndrome described in 2020, caused by mutations of the UBA1 gene, and displaying a large pleomorphic array of clinical and hematological features. Nevertheless, these criteria lack significance to discriminate VEXAS from othe...

A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

BMC medical informatics and decision making
BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence...