Hematology

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection are transforming traditional management approaches. This review explores how multiomics technologies and artificial intelligence (AI) are enhancing g...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural complications, which can increase healthcare costs. Developing efficient strategies for resource allocation has be...

Automated classification of elongated styloid processes using deep learning models-an artificial intelligence diagnostics.

BACKGROUND: The styloid process (SP), a bony projection from the temporal bone which can become elongated, resulting in cervical pain, throat discomfo...

Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is c...

Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.

BACKGROUND: Leukemia is the 11 most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood mali...

SIRE: Scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks.

The orientation of a blood vessel as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extrac...

Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment de...

Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.

OBJECTIVE: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict m...

Blood cancer prediction model based on deep learning technique.

Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early de...

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.

BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. T...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficienci...

Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from laten...

The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis.

BACKGROUND: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting end...

Uncovering blood-brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability.

This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blo...

Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.

Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout...

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.

Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of ...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-a...

Retrospective cohort study based on the MIMIC-IV database: analysis of factors influencing all-cause mortality at 30 days, 90 days, 1 year, and 3 years in patients with different types of stroke.

OBJECTIVE: This study aims to evaluate key factors influencing the short-term and long-term prognosis of stroke patients, with a particular focus on v...

Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning.

OBJECTIVE: This study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patie...