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Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

NeuroRehabilitation
BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at...

Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.

Journal of back and musculoskeletal rehabilitation
BACKGROUND: Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP.

Patients' perspectives on the use of artificial intelligence and robots in healthcare.

Bratislavske lekarske listy
OBJECTIVE: We aimed to evaluate the opinions of individuals aged 18 and above in our country regarding the use of artificial intelligence (AI) and robots in the field of healthcare.

Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

NeuroRehabilitation
BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait t...

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Journal of Alzheimer's disease : JAD
BACKGROUND: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack...

Updated Models of Alzheimer's Disease with Deep Neural Networks.

Journal of Alzheimer's disease : JAD
BACKGROUND: In recent years, researchers have focused on developing precise models for the progression of Alzheimer's disease (AD) using deep neural networks. Forecasting the progression of AD through the analysis of time series data represents a pro...

A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bi...

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

Current medical imaging
INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) imag...

Prostate Segmentation in MRI Images using Transfer Learning based Mask RCNN.

Current medical imaging
INTRODUCTION: The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it's the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, dee...

New training, new attitudes: non-clinical components in Ukrainian medical PHDs training (regarding critical thinking, academic integrity and artificial intelligence use).

Wiadomosci lekarskie (Warsaw, Poland : 1960)
OBJECTIVE: Aim: The paper studies the attitude to critical thinking, academic integrity and the Artificial Intelligence use of the Ukrainian medical PhD students.