Myeloma

Deep learning-based classifier for malignant plasma cell identification in myeloma.

Multiple myeloma (MM) displays significant genetic heterogeneity, making it challenging to distinguish malignant from non-malignant plasma cells in single-cell datasets. Existing marker-based and CNV detection methods require manual intervention or high computational resources, limiting their scope. We developed a supervised deep learning autoencoder to classify malignant cells across MM and its p...

Using artificial intelligence to identify characteristics associated with clinical and economic outcomes in MASH (FOCUS-MASH).

BACKGROUND: Heterogeneity in the disease characteristics of metabolic dysfunction-associated steatohepatitis (MASH) complicates efforts to identify individuals with high unmet need. OBJECTIVE: We used artificial intelligence (AI) phenotyping to identify factors associated with rapid fibrosis progression, long-term clinical outcomes, and high healthcare costs. DESIGN: In this retrospective cohort s...

Transformer-Based Deep Learning Model for Predicting Hemoglobin Response to Mircera® in Hemodialysis Patients.

BACKGROUND: Anemia management in hemodialysis (HD) depends on individualized erythropoiesis-stimulating agent (ESA) dosing to achieve and maintain tar...

AI derived Whole-Body MRI metrics in multiple myeloma patients reveal unique insights into body composition and outcomes.

Patients with multiple myeloma are frequently exposed to prolonged, multi-line and multi-drug treatment, factors that may substantially influence over...

Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

The hemoglobin concentration in blood is vital for diagnosing anemia and monitoring the various health conditions. However, conventional measurement m...

Forging New Pathways in Oncology: Strategic Insights from the 17th Annual Frontiers in Cancer Science Conference.

The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-spe...

Artificial Intelligence in Hemodialysis: Current Clinical Applications and Future Perspectives.

INTRODUCTION: Timely adjustment of intervention strategies based on multidimensional hemodialysis data is essential for improving patients' quality of...

Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.

BACKGROUND: Gestational anemia significantly elevates the risk of adverse maternal and neonatal outcomes, necessitating early predictive tools for tar...

Rapid delineation of Acinetobacter baumannii dose responses to zosurabalpin based on machine learning-assisted interpretation of bacterial nanomotions.

UNLABELLED: Carbapenem-resistant Acinetobacter baumannii (CRAB) represents a major clinical threat, underscoring the urgent need for both new antibiot...

Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study.

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (...

Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.

Anemia's high global prevalence and socio-economic burden necessitate early diagnosis, yet reliance on invasive blood testing creates significant barr...

A smart nutrition system for vitamin B12 deficiency management from sensing to precision intervention.

Vitamin B12 is essential for neural function, red blood cell formation, and DNA synthesis, yet its deficiency persists as a global health burden drivi...

Validation of a QuPath-Based Artificial Intelligence Workflow for Plasma Cell Quantification on CD138 Immunohistochemistry in an Indian Hematopathology Cohort.

INTRODUCTION: Plasma cell percentage on CD138 immunohistochemistry of bone marrow trephine biopsies is fundamental to the diagnosis and risk stratific...

A U-shaped robotic gripper with displacement and deformation controlled by programmable magnetic fields.

Magnetic soft robots are widely employed in micromanipulation applications because of their inherent biocompatibility, untethered actuation capabiliti...

Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.

BACKGROUND: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a major pathogen in hospital-acquired infections, and rapid detection is critical f...

Diagnostic performance of discriminant formulas and machine learning models for detecting β-thalassemia trait in Bangladesh.

BACKGROUND: β-thalassemia poses a considerable public health burden in Bangladesh, where a high carrier frequency underlies widespread disease risk. I...

Identification of Novel Diagnostic Biomarkers and Host-Directed Drug Screening for Mycobacterium avium Infection: A Multi-Omics and Artificial Intelligence Study.

OBJECTIVE: The rising incidence of Mycobacterium avium (MAV) infection poses significant challenges due to diagnostic delays and refractory treatment....

Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.

Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constra...

Age-stratified risk profiles for emergency colorectal cancer resection: A machine-learning analysis.

BACKGROUND: Emergency colorectal cancer resection (ECCR) is associated with worse perioperative and oncologic outcomes than elective surgery. As the i...

Large language models for generating longitudinal synthetic data in low-risk pregnancy care.

This article describes an obstetric dataset covering the full continuum of care of 5000 synthetic low-risk pregnant women, from preconception to post-...