Hematology

Life's essential 8 and its role in cardiovascular health: evolution, evidence, and future directions.

INTRODUCTION: Cardiovascular disease (CVD) remains the leading global cause of mortality. To advance prevention, the American Heart Association introduced Life's Simple 7 (LS7) in 2010 and updated it in 2022 to Life's Essential 8 (LE8). This paper reviews the development, scientific foundation, and predictive validity of LE8 across major U.S. and international cohorts, including its applications b...

Prediction of Type 2 Diabetes Mellitus From Chest X-Rays Using a Suite of Previously Developed Chronic Disease Deep Learning Models in an Ethnically Diverse Cohort: Observational Study.

BACKGROUND: Screening for type 2 diabetes (T2D) is not optimal, leading to a large number of patients being undiagnosed. Recently, deep learning (DL) applied to chest radiographs (CXRs) has shown promise for opportunistic T2D prediction. A prior study in a predominantly suburban non-Hispanic White cohort achieved an area under the curve (AUC) of 0.84 for prevalence. In this study, we evaluate the ...

A predictive model for pathological complete response in neoadjuvant-treated breast cancer: integrating pretreatment clinicopathologic and contrast-enhanced ultrasound characteristics via interpretable machine learning.

BACKGROUND: The ability to predict pathological complete response (pCR) prior to neoadjuvant therapy (NAT) would inform and facilitate tailored therap...

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...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To ...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer fro...

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...

Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.

Metastatic renal cell carcinoma remains clinically challenging because of heterogeneous outcomes and limited predictive biomarkers for immunotherapy. ...

Geometric-structural multi-label learning for non-invasive prediction of breast cancer biomarkers.

Accurate profiling of molecular biomarkers, including ER, PR, HER2, and Ki-67, is pivotal for tailoring therapeutic strategies in breast cancer manage...

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...

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-gener...

SAP18 drives vasculogenic mimicry in esophageal squamous cell carcinoma: a machine learning and multi-omics investigation.

Vasculogenic mimicry (VM), a novel endothelial-independent blood perfusion pathway, is linked to advanced stage and poor prognosis in esophageal squam...

Developing a machine learning model to predict renal function decline within three years using one-year longitudinal data in patients with type 2 diabetes.

AIMS: The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to an increase in diabetic kidney disease (DKD), which is presently a major...

Prioritising parameters to minimise blood transfusions for postpartum haemorrhage: A machine learning study using national population data.

BACKGROUND: Postpartum haemorrhage (PPH) accounts for approximately 27% of maternal deaths worldwide and often requires blood transfusion, along with ...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model.

BACKGROUND: Prognostic assessment in critically ill cancer patients is challenging due to the suboptimal performance of traditional severity scores. W...

Edge-prior and reliability-guided collaborative learning for white blood cell classification.

Accurate white blood cell (WBC) classification is important for hematological screening and computer-aided blood smear image analysis. Manual microsco...

When artificial intelligence meets musculoskeletal ultrasound in hemophilia.

AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.

Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in...

An Explainable Machine Learning Model for Early Prediction of Incident Myocardial Injury in Patients With Severe Fever With Thrombocytopenia Syndrome.

This study aimed to develop an explainable machine learning-based model to enable early prediction of incident myocardial injury during hospitalizatio...

Advances and prospects in cell therapy for cancer: explorations from T cells to stem cells.

The increasing global burden of cancer necessitates innovative therapeutic strategies. Cell therapy represents a major breakthrough in oncology, evolv...