Hematology

Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

PURPOSE: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This work explores the utility of a NCCTH bone removal algorithm for improving SDH detection.

MultiThal-classifier, a machine learning-based multi-class model for thalassemia diagnosis and classification.

BACKGROUND: The differential diagnosis between iron deficiency anemia (IDA) and thalassemia trait (TT) remains a significant clinical challenge. This study aimed to develop a machine learning-based multi-class model to differentiate among Microcytic-TT(TT with low mean corpuscular volume), Normocytic-TT (TT with normal mean corpuscular volume), IDA, and healthy individuals.

A machine-learning-based algorithm for bone marrow cell differential counting.

BACKGROUND: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases....

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning.

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy techni...

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting m...

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using eryth...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research...

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in r...

An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.

This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR...

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using ...

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG.

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this p...

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

BACKGROUND: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T...

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were develo...

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.

Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral...

Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.

BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.

Optimized deep learning networks for accurate identification of cancer cells in bone marrow.

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-...

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune...

Rapid detection of drugs in blood using "molecular hook" surface-enhanced Raman spectroscopy and artificial intelligence technology for clinical applications.

Accurate detection of cardiovascular drugs in blood is complicated by interference from serum biomolecules. This study introduces a novel surface-enha...

Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy.

This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption ...

Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection.

OBJECTIVE: To evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients comp...