AI Medical Compendium Journal:
Blood

Showing 1 to 10 of 10 articles

Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management.

Blood
Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly r...

Spatial mapping of human hematopoiesis at single-cell resolution reveals aging-associated topographic remodeling.

Blood
The spatial anatomy of hematopoiesis in the bone marrow (BM) has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain tho...

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

Blood
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times e...

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.

Blood
Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical su...

Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Blood
Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, i...

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Blood
Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features...