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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Broadening the horizon: potential applications of CAR-T cells beyond current indications.

Frontiers in immunology
Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell therapies in B-ALL. Several diseases can now be treated in highly therapy-refractory or relapsed condit...

Humoral Immune Responses after an Omicron-Adapted Booster BNT162b2 Vaccination in Patients with Lymphoid Malignancies.

Viruses
To accommodate waning COVID-19 vaccine immunity to emerging SARS-CoV-2 variants, variant-adapted mRNA vaccines have been introduced. Here, we examine serological responses to the BA.1 and BA.4-5 Omicron variant-adapted BNT162b2 COVID-19 vaccines in p...

Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia.

Scientific reports
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients' response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progr...

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...

Class-imbalanced complementary-label learning via weighted loss.

Neural networks : the official journal of the International Neural Network Society
Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios, the number of samples in one...

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

PLoS computational biology
The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are importan...

A Conformal Prediction Approach to Enhance Predictive Accuracy and Confidence in Machine Learning Application in Chronic Diseases.

Studies in health technology and informatics
Heterogeneity in chronic malignancies raises an increasing interest for the integration and study of predictive models. This study presents a machine learning model approach to predict outcomes and improve their trustworthiness in multi-factorial dis...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...

Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia.

Studies in health technology and informatics
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting s...