AIMC Topic: Middle Aged

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Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

Computer methods and programs in biomedicine
INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

A preliminary study on the effects of long-term robot suit exercise training on gait function and quality of life in patients with spinal and bulbar muscular atrophy.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Spinal and bulbar muscular atrophy (SBMA) progressively impairs gait function, resulting in the need for patients to use a wheelchair approximately 20 years after onset. No reports have investigated the effects of long-term exercise training using th...

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Environmental toxicology
BACKGROUND: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognos...

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Sensors (Basel, Switzerland)
Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pen...

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)-A Pilot Study.

Nutrients
Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications and readmission. Diagnosis is currently based on clinical guidelines, which includes assessment of sk...

Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.

Scientific reports
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations u...

Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125.

Scientific reports
Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments...

Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning.

Scientific reports
Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time...

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Nature communications
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present ...