AIMC Topic: Humans

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Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

International journal of molecular sciences
Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseases, and neurodegeneration, which have no fixed three-dimensional structure under physiological condi...

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.

Journal of neuroengineering and rehabilitation
BACKGROUND: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising appro...

Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.

Scientific reports
Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform fo...

A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.

Scientific reports
Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and resp...

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

Physics in medicine and biology
A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for a...

How will artificial intelligence transform cardiovascular computed tomography? A conversation with an AI model.

Journal of cardiovascular computed tomography
Artificial intelligence (AI) has the potential to transform healthcare, but its clinical use also has important challenges and limitations. Recently natural language processing and generative pre-training transformer (GPT) models have gained particul...

Dynamic three-sided matching model for personnel-robot-position matching problem in intelligent environments.

PloS one
In recent years, intelligent robots have facilitated intelligent production, and a new type of problem (personnel-robot-position matching (PRPM)) has been encountered in personnel-position matching (PPM). In this study, a dynamic three-sided matching...

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Molecular diversity
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major ...

The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients.