AIMC Topic: Neural Networks, Computer

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Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network.

Journal of biomechanics
Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automati...

A robust deep learning model for the classification of dental implant brands.

Journal of stomatology, oral and maxillofacial surgery
OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for t...

Radiographic morphology of canines tested for sexual dimorphism via convolutional-neural-network-based artificial intelligence.

Morphologie : bulletin de l'Association des anatomistes
The permanent left mandibular canines have been used for sexual dimorphism when human identification is necessary. Controversy remains whether the morphology of these teeth is actually useful to distinguish males and females. This study aimed to asse...

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. There...

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

European journal of internal medicine
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA...

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Medical & biological engineering & computing
Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach in...

The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

Indian journal of ophthalmology
Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially i...

Automated Spontaneous Echo Contrast Detection Using a Multisequence Attention Convolutional Neural Network.

Ultrasound in medicine & biology
OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increased thromboembolism risk. However, identification requires expert determination and clinician time to report. We developed a deep learning model that ca...

Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

International journal of biometeorology
In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded...

Modular Spiking Neural Membrane Systems for Image Classification.

International journal of neural systems
A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of ...