AI Medical Compendium Journal:
Haematologica

Showing 1 to 5 of 5 articles

Advances in next-generation sequencing and emerging technologies for hematologic malignancies.

Haematologica
Innovations in molecular diagnostics have often evolved through the study of hematologic malignancies. Examples include the pioneering characterization of the Philadelphia chromosome by cytogenetics in the 1970s, the implementation of polymerase chai...

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...

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.

Haematologica
Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of it...

Machine learning reveals chronic graft--host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.

Haematologica
The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft--host disease. Chronic graft--host disease is classified by an...

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Haematologica
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856...