AIMC Topic: Deep Learning

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Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Radiology
Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time consuming. Purpose To develop and validate a deep learning (DL) model for automated CTO reconstructio...

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis.

Radiology
Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of...

Rapid proximate analysis of coal based on reflectance spectroscopy and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slo...

Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.

Journal of the mechanical behavior of biomedical materials
INTRODUCTION: One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechanical loading. The current practice for evaluating bone microdamage is to quantify damage volume from ...

Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.

Computers in biology and medicine
BACKGROUND: An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle-Invasive Bladder Cancer (MIBC) in Bladder Cancer (BCa) can help the urologist make diagnostic decisions. Considering the absence of multiparametr...

D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.

Computers in biology and medicine
The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human an...

Prior optic neuritis detection on peripapillary ring scans using deep learning.

Annals of clinical and translational neurology
BACKGROUND: The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes...

Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos.

BMC oral health
BACKGROUND: Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the...

Explaining neural activity in human listeners with deep learning via natural language processing of narrative text.

Scientific reports
Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodin...