Hematology

Lymphoma

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

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An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for b...

Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling.

Dynamic variables contribute to understand the mechanics of pedalling and can assist with injury pre...

Machine Learning Predicts Non-Preferred and Preferred Vertebrate Hosts of Tsetse Flies (Glossina spp.) Based on Skin Volatile Emission Profiles.

Tsetse fly vectors of African trypanosomosis preferentially feed on certain vertebrates largely dete...

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditio...

Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.

Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-relat...

Temporal Contrastive Learning through implicit non-equilibrium memory.

The backpropagation method has enabled transformative uses of neural networks. Alternatively, for en...

Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI.

Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly...

Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.

BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possi...

Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.

AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non...

Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.

Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant...

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours.

BACKGROUND: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, ...

Developing a real-time water quality simulation toolbox using machine learning and application programming interface.

Rivers are vital for sustaining human life as they foster social development, provide drinking water...

Development and evaluation of USCnet: an AI-based model for preoperative prediction of infectious and non-infectious urolithiasis.

BACKGROUND: Urolithiasis, a prevalent condition characterized by a high rate of incidence and recurr...

Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Oleogelators are considered food additives that require approval from regulatory authorities. Theref...

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are o...

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care un...

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