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Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.
Abdominal radiology (New York)
May 26, 2024
PURPOSE: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.
Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.
JPEN. Journal of parenteral and enteral nutrition
May 26, 2024
BACKGROUND: The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a de...
An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel.
Medicina (Kaunas, Lithuania)
May 26, 2024
: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson's disease (PD) under levodopa-carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict th...
Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression.
NeuroImage. Clinical
May 26, 2024
Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multipl...
A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
May 25, 2024
BACKGROUND: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance w...
Deep learning predicts postoperative opioids refills in a multi-institutional cohort of surgical patients.
Surgery
May 25, 2024
BACKGROUND: To combat the opioid epidemic, several strategies were implemented to limit the unnecessary prescription of opioids in the postoperative period. However, this leaves a subset of patients who genuinely require additional opioids with inade...
Preliminary study on the ability of the machine learning models based on F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma.
European journal of radiology
May 25, 2024
PURPOSE: The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati...
Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.
Scientific reports
May 25, 2024
This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals),...
Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.
BMC neuroscience
May 25, 2024
INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatme...
Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease.
Ear and hearing
May 24, 2024
OBJECTIVES: This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore...
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