AIMC Topic: Humans

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Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study.

BMC medical informatics and decision making
BACKGROUND: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result f...

A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images.

BMC medical imaging
PURPOSE: To compare a deep learning model with a radiomics model in differentiating high-grade (LR-3, LR-4, LR-5) liver imaging reporting and data system (LI-RADS) liver tumors from low-grade (LR-1, LR-2) LI-RADS tumors based on the contrast-enhanced...

Predicting multipotency of human adult stem cells derived from various donors through deep learning.

Scientific reports
Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidifferentiation capacity. Nevertheless, the outcomes of stem cell‑based therapies to date have shown inco...

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare.

Scientific reports
Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotel...

Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning.

Journal of orthopaedic surgery and research
BACKGROUND: For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.

Electronic whiskers for velocity sensing based on the liquid metal hysteresis effect.

Soft matter
The artificial biomimetic sensory hair as state-of-art electronics has drawn great attention from academic theorists of industrial production given its potential application in soft robotics, environmental exploration and health monitoring. However, ...

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Genomics, proteomics & bioinformatics
Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and pheno...

Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.

PloS one
We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the dr...

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

Journal of computer assisted tomography
Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented vari...

Making and selecting the best embryo in the laboratory.

Fertility and sterility
Over the past 4 decades our ability to maintain a viable human embryo in vitro has improved dramatically, leading to higher implantation rates. This has led to a notable shift to single blastocyst transfer and the ensuing elimination of high order mu...