AIMC Topic: Female

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Specific Instructions Are Important: A Cross-sectional Study on Device Parameters and Instruction Types While Walking With a Robot in Children and Adolescents.

American journal of physical medicine & rehabilitation
OBJECTIVE: The aim of the study is to evaluate how gait kinematics and muscle activity during robot-assisted gait training are affected by different combinations of parameter settings and a number of instruction types, ranging from no instructions to...

Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.

Journal of sport and health science
BACKGROUND: There exist few maximal oxygen uptake (VO) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO is infeasible in large epidemiologic cohort stud...

Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.

Journal of imaging informatics in medicine
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Af...

Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction.

Academic radiology
RATIONALE AND OBJECTIVE: To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions.

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine le...

Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.

Science bulletin
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predic...

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities.

Veterinary journal (London, England : 1997)
Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to incon...

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BMC bioinformatics
BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome predict...

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

Psychotherapy research : journal of the Society for Psychotherapy Research
OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Process...