BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber v...
Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to lim...
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed t...
BACKGROUND: Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution ...
The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no ...
This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LET) of protons in proton-beam therapy based on the planned dose distribution and patien...
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Aug 12, 2024
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machin...
PURPOSE: This study aimed to develop an algorithm for the automatic detecting chest percutaneous catheter drainage (PCD) and evaluating catheter positions on chest radiographs using deep learning.
The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, ...
PURPOSE: Echo modulation curve (EMC) modeling enables accurate quantification of T relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary...
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