AIMC Topic: Aged

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Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

Radiation oncology (London, England)
PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictiv...

Robot-aided assessment and associated brain lesions of impaired ankle proprioception in chronic stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Impaired ankle proprioception strongly predicts balance dysfunction in chronic stroke. However, only sparse data on ankle position sense and no systematic data on ankle motion sense dysfunction in stroke are available. Moreover, the lesio...

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BMC pulmonary medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment p...

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).

Scientific reports
Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationshi...

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.

Scientific reports
Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce productivity. Exercise therapy is the gold standard treatment for MSDs and can be provided by physiotherapists and/or also via mobile apps. Apart from the ...

Prediction of hepatic metastasis in esophageal cancer based on machine learning.

Scientific reports
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) databas...

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning.

Anais da Academia Brasileira de Ciencias
The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This ...

Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson's disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. ...

Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.

Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonan...