Hematology

Classification of α-thalassemia data using machine learning models.

BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-car...

An effective vessel segmentation method using SLOA-HGC.

Accurate segmentation of retinal blood vessels from retinal images is crucial for detecting and diagnosing a wide range of ophthalmic diseases. Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. Channel-aware self-attenti...

Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, ane...

Prediction of pulmonary embolism by an explainable machine learning approach in the real world.

In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge beca...

The clinical features and risk factors of coagulopathy associated with cefoperazone/sulbactam: a nomogram prediction model.

BACKGROUND: Cefoperazone/sulbactam (CPZ/SAM) is an important treatment option for infections caused by multidrug-resistant gram-negative bacteria. How...

Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.

As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the n...

Deep learning-based discovery of compounds for blood pressure lowering effects.

The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively p...

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.

Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inc...

Vitamin D deficiency among pregnant women in India.

Vitamin D is a fat-soluble vitamin involved in the regulation of calcium homeostasis and thus is essential for a healthy skeletal system. The blood sa...

Quantitative imaging for early detection and risk stratification of cardiovascular disease using 4D flow MRI and arterial spin labelling.

Heart failure (HF) significantly burdens global healthcare, necessitating early detection and precise risk stratification. Advanced imaging techniques...

Neuromorphic-enabled video-activated cell sorting.

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition....

Evaluation of the effect of cell freshness on pyrogen detection using a serum-free monocyte-activation test.

Pyrogens cause shock symptoms when released into the bloodstream. They are classified into two main categories: endotoxins (lipopolysaccharides [LPS])...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia p...

A machine learning tool for early identification of celiac disease autoimmunity.

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagno...

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.

A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel a...

Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.

: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the ri...

Elevated Vitamin B12 Levels in Myeloproliferative Neoplasm (MPN) Patients: A Potential Diagnostic and Prognostic Marker.

BACKGROUND: Elevated vitamin B12 (B12) levels are linked to an increased risk of cancers, including hematological malignancies. This study focuses on ...

The Hemodynamic Management and Postoperative Outcomes After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Prospective Observational Study.

Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has become standard treatment for peritoneal cancers and metastases,...

MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.

We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning...

Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair.

PURPOSE: In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important t...