Hematology

Latest AI and machine learning research in hematology for healthcare professionals.

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Improving blood cells classification in peripheral blood smears using enhanced incremental training.

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by medical specialists to ...

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.

BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute ...

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.

We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complicati...

Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications.

Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to...

Cerebral blood flow measurements with O-water PET using a non-invasive machine-learning-derived arterial input function.

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of O-label...

BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells.

BACKGROUND AND OBJECTIVE: The classification of human peripheral blood cells yields significance in ...

Autonomous Robot for Removing Superficial Traumatic Blood.

: To remove blood from an incision and find the incision spot is a key task during surgery, or else ...

Physical Features and Vital Signs Predict Serum Albumin and Globulin Concentrations Using Machine Learning.

OBJECTIVE: Serum protein concentrations are diagnostically and prognostically valuable in cancer and...

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques.

Heart angiography is a test in which the concerned medical specialist identifies the abnormality in ...

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of ...

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gast...

Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

Our previous research shows that structured cancer DX description data accuracy varied across electr...

A microfluidic robot for rare cell sorting based on machine vision identification and multi-step sorting strategy.

The identification, sorting and analysis of rare target single cells in human blood has always been ...

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characteriz...

Deep transfer learning approaches for bleeding detection in endoscopy images.

Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed rapidly over ...

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers fo...

Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method.

BACKGROUND AND OBJECTIVE: The purpose of this study was to develop a quantitative structure-activity...

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