AIMC Topic: Middle Aged

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Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high-risk populations. In this multicenter study, plasma samples are collected from patients with stable angina after PCI...

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Scientific reports
Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response...

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

Parkinsonism & related disorders
INTRODUCTION: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonanc...

Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units.

Sensors (Basel, Switzerland)
This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is ...

Cardiac Operative Risk in Latin America: A Comparison of Machine Learning Models vs EuroSCORE-II.

The Annals of thoracic surgery
BACKGROUND: Machine learning is a useful tool for predicting medical outcomes. This study aimed to develop a machine learning-based preoperative score to predict cardiac surgical operative mortality.

Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.

Academic radiology
OBJECTIVES: To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data.

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage.

World neurosurgery
BACKGROUND: Immediate and accurate detection of intracranial hemorrhages (ICHs) is essential to provide a good clinical outcome for patients with ICH. Artificial intelligence has the potential to provide this, but the assessment of these methods need...

Individualized prediction of COVID-19 adverse outcomes with MLHO.

Scientific reports
The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, includi...

A randomized controlled trial on the effects induced by robot-assisted and usual-care rehabilitation on upper limb muscle synergies in post-stroke subjects.

Scientific reports
Muscle synergies are hypothesized to reflect connections among motoneurons in the spinal cord activated by central commands and sensory feedback. Robotic rehabilitation of upper limb in post-stroke subjects has shown promising results in terms of imp...

MRI-Based Deep-Learning Method for Determining Glioma Promoter Methylation Status.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: () promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining promoter methylation status using T2 weighted Images (T2WI) only.