Hematology

Lymphoma

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

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A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a si...

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients ...

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and tradi...

Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.

BACKGROUND: The study's objective was to develop an automated method for a histopathology recognitio...

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinica...

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly...

A review of machine learning methods for non-invasive blood pressure estimation.

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodyn...

Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks.

PURPOSE: This study aims to seek an optimized deep learning model for differentiating non-traumatic ...

GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics.

The world today has made prescriptive analytics that uses data-driven insights to guide future actio...

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.

BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (C...

An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI.

Some pathologies such as cancer and dementia require multiple imaging modalities to fully diagnose a...

Artificial Neural Network analysis on the effect of mixed convection in triangular-shaped geometry using water-based Al2O3 nanofluid.

The objective of the study is to investigate the fluid flow and heat transfer characteristics applyi...

Comparison and benchmark of deep learning methods for non-coding RNA classification.

The involvement of non-coding RNAs in biological processes and diseases has made the exploration of ...

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastas...

MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.

Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement ...

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy.

This narrative review examines the promising potential of integrating artificial intelligence (AI) w...

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