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A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.
BMC medical informatics and decision making
Feb 11, 2025
BACKGROUND: Screening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed an optimal screening model for malignant hematological diseases based on routine blood cell paramete...
Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.
Scientific reports
Feb 11, 2025
Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large om...
Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.
Journal of medical systems
Feb 11, 2025
This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) ...
Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.
PloS one
Feb 11, 2025
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted...
Prediction of High-risk Capsule Characteristics for Recurrence of Pleomorphic Adenoma in the Parotid Gland Based on Habitat Imaging and Peritumoral Radiomics: A Two-center Study.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: This study aims to develop and validate an ultrasoundbased habitat imaging and peritumoral radiomics model for predicting high-risk capsule characteristics for recurrence of pleomorphic adenoma (PA) of the parotid gland whil...
Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients.
Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.
Interdisciplinary sciences, computational life sciences
Feb 10, 2025
PURPOSE: With the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging population and the nature of chronic illnesses, patients often suffer from multimorbidity. Predicting th...
Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.
Magnetic resonance imaging
Feb 10, 2025
OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image qua...
Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging ...
Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.
Nature medicine
Feb 10, 2025
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting ...
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