AIMC Topic: Deep Learning

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Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

Korean journal of radiology
OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.

Assessment of glomerular morphological patterns by deep learning algorithms.

Journal of nephrology
BACKGROUND: Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures ...

Deep learning for subtyping the Alzheimer's disease spectrum.

Trends in molecular medicine
In a recent article from Cell Reports Medicine, Kwak et al. generate novel insights about subtyping cognitively impaired individuals based on structural imaging. Quantifying heterogeneity in Alzheimer's disease via subtyping could help us harness new...

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model.

Sensors (Basel, Switzerland)
With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tum...

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity.

Sensors (Basel, Switzerland)
Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is contr...

Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition.

Sensors (Basel, Switzerland)
As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved...

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Ophthalmology
PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

Analytical chemistry
The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the ...

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and...

Screening Anti-inflammatory, Anticoagulant, and Respiratory Agents for SARS-CoV-2 3CL Inhibition from Chemical Fingerprints Through a Deep Learning Approach.

Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 2019 (COVID-19), triggers a pathophysiological process linked not only to viral mechanisms of infectivity, but also to the pattern of...