AIMC Topic: Middle Aged

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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)
: 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...

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

Sensors (Basel, Switzerland)
In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystag...

The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives.

Scientific reports
Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis....

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
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
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...

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders.

NeuroImage
Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders...

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
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...

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

BMC neuroscience
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
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...

Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetr...