Myeloma

Establishment of a Multimodal Prognostic Prediction Model for Multiple Myeloma Patients Based on Radiomics and Clinical Features: A Retrospective Cohort Study.

OBJECTIVE: We aimed to develop a survival prediction system integrating radiomics and clinical features for newly diagnosed multiple myeloma (MM), and to compare the performance of different feature sets and algorithms in early prediction of progression-free survival (PFS). METHODS: This study retrospectively included 300 MM patients between June 2022 and June 2024, with their baseline positron em...

Personalized survival prediction in elderly dialysis patients: integrating machine learning with traditional survival analysis.

BACKGROUND: Elderly patients undergoing kidney replacement therapy (KRT) face high mortality rates. Traditional statistical models describe overall survival patterns but may fail to capture non-linear relationships among clinical factors. This study integrates classical survival analysis and machine learning (ML) to enhance individualized survival prediction in elderly patients treated with hemodi...

An ensemble machine learning approach for predicting anemia among under-five children in malaria-endemic sub-Saharan African countries.

BACKGROUND: Worldwide, anemia in children under-five is a major public health issue, particularly in sub-Saharan Africa. Sub-Saharan Africa also has t...

Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demons...

LWF-YOLO: a lightweight framework based YOLO for blood cell detection.

Accurate detection of blood cells-red blood cells, white blood cells, and platelets-is essential for diagnosing hematological disorders such as anemia...

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 si...

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 in...

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...

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...