AIMC Topic: Deep Learning

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Machine learning applications for early detection of esophageal cancer: a systematic review.

BMC medical informatics and decision making
INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients...

Unsupervised abnormality detection in neonatal MRI brain scans using deep learning.

Scientific reports
Analysis of 3D medical imaging data has been a large topic of focus in the area of Machine Learning/Artificial Intelligence, though little work has been done in algorithmic (particularly unsupervised) analysis of neonatal brain MRI's. A myriad of con...

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Scientific reports
Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of deep learning models that predict the onset of cancer pain exacerbation in hospitalized patients. We...

Deep learning neural network derivation and testing to distinguish acute poisonings.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was e...

Deep Learning Classification of Angle Closure based on Anterior Segment OCT.

Ophthalmology. Glaucoma
PURPOSE: To assess the performance and generalizability of a convolutional neural network (CNN) model for objective and high-throughput identification of primary angle-closure disease (PACD) as well as PACD stage differentiation on anterior segment s...

Deep learning in preclinical antibody drug discovery and development.

Methods (San Diego, Calif.)
Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and ...

Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology.

Journal of biophotonics
Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep-learning methods have...

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.

Viruses
Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural ...

Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.

BMC medical informatics and decision making
BACKGROUND: Histological assessment of smooth muscle is a critical step particularly in staging malignant tumors in various internal organs including  the urinary bladder. Nonetheless, manual segmentation and classification of muscular tissues by pat...

Deep learning for artery-vein classification in optical coherence tomography angiography.

Experimental biology and medicine (Maywood, N.J.)
Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal ...