AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Leukemia, Lymphocytic, Chronic, B-Cell

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Machine learning applications in the diagnosis of leukemia: Current trends and future directions.

International journal of laboratory hematology
Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing tr...

Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Nature communications
Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop...

Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self-organiz...

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...

Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-art.

Leukemia & lymphoma
Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient's bedside for AI assisted interventions is not yet the norm. With ...

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

The Journal of pathology
Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnosti...

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Archives of pathology & laboratory medicine
CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relative...

Quantitative features to assist in the diagnostic assessment of chronic lymphocytic leukemia progression.

The Journal of pathology
The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays...

Potential for Process Improvement of Clinical Flow Cytometry by Incorporating Real-Time Automated Screening of Data to Expedite Addition of Antibody Panels.

American journal of clinical pathology
OBJECTIVES: We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.