AIMC Topic: Deep Learning

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A CSI-Based Human Activity Recognition Using Deep Learning.

Sensors (Basel, Switzerland)
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-ba...

Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence.

Journal of electrocardiology
BACKGROUND: Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnost...

Graphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies.

The Science of the total environment
In our previous studies, it was found that graphene oxide (GO) reduced the endocrine disruption of bisphenol A (BPA) in zebrafish embryo and larvae, but through different mechanisms. In this study, adult male zebrafish were selected to further unders...

The AlphaFold Database of Protein Structures: A Biologist's Guide.

Journal of molecular biology
AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the...

A deep learning model for burn depth classification using ultrasound imaging.

Journal of the mechanical behavior of biomedical materials
Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ult...

Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm.

Magnetic resonance imaging
OBJECTIVE: To assess a new deep learning-based MR reconstruction method, "DLRecon," for clinical evaluation of peripheral nerves.

Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning.

Advances in biological regulation
Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to multiple mechanisms and exposure to diverse environments. These disorders further increase gene mutation...

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Computers in biology and medicine
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation pro...

Automated chart filing on panoramic radiographs using deep learning.

Journal of dentistry
OBJECTIVE: The aim of this study is to automatically detect, segment and label teeth, crowns, fillings, root canal fillings, implants and root remnants on panoramic radiographs (PR(s)).

Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.

Arthritis & rheumatology (Hoboken, N.J.)
OBJECTIVE: To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.