Hematology

Leveraging Kappa-Lambda Signatures in a Multi-Stage Machine Learning Pipeline for B-Cell Lymphoma Detection by Flow Cytometry.

Flow cytometry immunophenotyping is essential for diagnosing B-cell lymphomas, but manual interpretation of high-dimensional data remains subjective, time-consuming, and prone to inter-operator variability. Previous computational approaches often overlook clinically relevant principles such as immunoglobulin light chain restriction. To address this gap, a biologically informed, three-stage machine...

From Antigen to Atlas: A Multi-omics, Single-Cell Pipeline for Discovering Safe and Effective CAR Targets in Ovarian Cancer.

Chimeric antigen receptor (CAR) T-cell therapy has achieved remarkable success in hematologic malignancies but continues to face significant barriers in solid tumors such as ovarian cancer (OC), primarily due to antigen heterogeneity and on-target, off-tumor toxicity. Traditional discovery methods that rely on bulk tissue analyses are inadequate to overcome these challenges. This review proposes a...

Machine Learning-Enhanced Microfluidic Impedance Platform for Rare Cell Analysis.

Rare cells, despite constituting only a small fraction of the population, play a critical role in health and disease. For instance, a minor subset of ...

Identifying patients at risk for short-term adverse events after hip arthroscopy: a machine learning analysis of a national database.

PURPOSE: To develop and compare machine learning-based risk prediction models to identify patients at risk for short-term adverse outcomes (overnight ...

Human-in-the-loop validation of an artificial intelligence-driven real-time prostatic capsule recognition model for nerve-sparing robot-assisted radical prostatectomy.

BACKGROUND: Nerve-sparing robot-assisted radical prostatectomy (NS-RARP) requires precise prostatic capsule identification to balance oncological cont...

Field Foresight-Predicting the Need for Massive Transfusion, ICU Utilization, and Mechanical Ventilation for Traumatically Injured Patients.

INTRODUCTION: Expeditiously predicting outcomes is essential to allocating blood and intensive care resources. We hypothesize the use of external inju...

Intelligent model for stratification of acute radiation syndrome severity in humans.

PURPOSE: The aim of this study was to develop and compare two intelligent model for stratifying the severity of acute radiation syndrome (ARS) in huma...

Noninvasive Blood-Based Detection of Endometriosis Can Improve Standard-of-Care by Facilitating Early Diagnosis and Clinical Management Among Symptomatic Women.

STUDY OBJECTIVE: To develop and validate a non-invasive, blood-based diagnostic assay for endometriosis that performs accurately across menstrual cycl...

Artificial intelligence for automated detection of joint bleeding via ultrasound in hemophilia: advancing standardization.

BACKGROUND: Musculoskeletal ultrasound (US) is a noninvasive tool for joint assessment in persons with hemophilia. Early detection of joint bleeding u...

A multi-feature alignment fusion neural network model for red blood cell aggregation classification using ultrasonic radiofrequency data of blood.

Evaluation of red blood cell (RBC) aggregation is crucial for the early prevention and accurate diagnosis of diseases such as ischemic cardiovascular ...

CAR T-cells in hematologic malignancies: Advances, challenges, and future directions.

Chimeric antigen receptor (CAR) T-cell therapy has transformed the management of hematologic malignancies, achieving high remission rates in relapsed ...

Expectations and Limitations of Artificial Intelligence in Blood Cancer Diagnosis.

In this commentary, we open the debate on what can be expected from artificial intelligence (AI) in the diagnosis of hematologic cancers. We discuss t...

Maternal Gut Microbiome as a Predictor of Insulin Therapy Requirement in Gestational Diabetes.

BACKGROUND: Gestational diabetes mellitus (GDM) often requires pharmacological intervention beyond lifestyle modification to achieve optimal glycemic ...

Comparison of the diagnostic performance of machine learning algorithms for differentiating iron deficiency anemia and thalassemia.

Machine learning-based multiclass model for autoimmune disease diagnosis and classification through nailfold videocapillaroscopy features.

OBJECTIVE: To develop and validate a predictive model for distinguishing controls (Ctr), rheumatoid arthritis (RA) and systemic lupus erythematosus (S...

Correspondence on "Evaluation of the impact of NOAC underdosing and exploration of bleeding risk factors in elderly patients with atrial fibrillation: artificial intelligence-based approach" by Protzenko et al.

Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis.

Urosepsis is a severe complication of urinary tract infection (UTI) and may lead to organ dysfunction and death. Early identification remains challeng...

Cell-free chromatin state tracing reveals disease origin and therapy responses.

Cell-free DNA in blood originates from fragmented chromatin released by dying cells from both healthy and diseased tissues1,2. These fragments carry r...

Determinants of labor epidural analgesia uptake and associations with maternal-neonatal outcomes: a stratified cohort study with risk prediction modeling.

BACKGROUND: Labor pain is a major physiological and psychological stressor for women during childbirth. Epidural analgesia is widely recognized as the...

Plasma proteomic profiles of lung volume-based phenotypes in Tobacco-Exposed Individuals Without Spirometric COPD.

BACKGROUND: Among individuals with a history of smoking but preserved spirometry (tobacco exposed with preserved spirometry, or TEPS), lung volume-bas...