AIMC Topic: Humans

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Deep Learning of Videourodynamics to Classify Bladder Dysfunction Severity in Patients With Spina Bifida.

The Journal of urology
PURPOSE: Urologists rely heavily on videourodynamics to identify patients with neurogenic bladders who are at risk of upper tract injury, but their interpretation has high interobserver variability. Our objective was to develop deep learning models o...

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.

Journal of nephrology
OBJECTIVES: In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the a...

Improvement of Precision Grasping Performance by Interaction Between Soft Finger Pulp and Hard Nail.

Soft robotics
In this study, we investigated the effect of the presence or absence of fingernails on precision grasping using artificial anthropomimetic fingers. We hypothesized that fingernails improve precision grasping performance by increasing the friction coe...

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Radiology
Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function w...

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours.

Sensors (Basel, Switzerland)
Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during l...

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

BMC bioinformatics
BACKGROUND: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various s...

A multimodal deep learning model to infer cell-type-specific functional gene networks.

BMC bioinformatics
BACKGROUND: Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships an...

Hybrid autoencoder with orthogonal latent space for robust population structure inference.

Scientific reports
Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts a...

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies.

Traffic injury prevention
OBJECTIVE: The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomo...

An Extended Spatial Transformer Convolutional Neural Network for Gesture Recognition and Self-Calibration Based on Sparse sEMG Electrodes.

IEEE transactions on biomedical circuits and systems
sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique advantages. However, the accuracy of recognition drops significantly as electrodes shift. Besides, in applications such as VR, virtual hands should be ...