AIMC Topic: Deep Learning

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Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models.

Journal of stomatology, oral and maxillofacial surgery
BACKGROUND: Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine s...

Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal t...

Which images to label for few-shot medical image analysis?

Medical image analysis
The success of deep learning methodologies hinges upon the availability of meticulously labeled extensive datasets. However, when dealing with medical images, the annotation process for such abundant training data often necessitates the involvement o...

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.

Annals of biomedical engineering
In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link be...

Deep magnetic resonance fingerprinting based on Local and Global Vision Transformer.

Medical image analysis
To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the numbe...

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

European journal of obstetrics, gynecology, and reproductive biology
PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images...

The effect of head motion on brain age prediction using deep convolutional neural networks.

NeuroImage
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted ag...

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

The Plant journal : for cell and molecular biology
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genot...

Deep neural network model for enhancing disease prediction using auto encoder based broad learning.

SLAS technology
Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ...

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning.

Journal of chemical information and modeling
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from a...