Primary Care

Preventive Care

Latest AI and machine learning research in preventive care for healthcare professionals.

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Automated retinopathy of prematurity screening using deep neural networks.

BACKGROUND: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. ...

RASPELD to Perform High-End Screening in an Academic Environment toward the Development of Cancer Therapeutics.

The identification of compounds for dissecting biological functions and the development of novel dru...

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Polyps in the colon can potentially become malignant cancer tissues where early detection and remova...

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.

BACKGROUND: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplas...

Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly p...

Robotic selection for the rapid development of stable CHO cell lines for HIV vaccine production.

The production of envelope glycoproteins (Envs) for use as HIV vaccines is challenging. The yield of...

Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data.

Chemogenomic approaches involving highly annotated compound sets and cell based high throughput scre...

Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-spec...

Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.

Early detection of cancer can increase patients' survivability and treatment options. Medical images...

A new computational intelligence approach to detect autistic features for autism screening.

Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disability diagnosis. Gen...

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Machine learning (ML) algorithms are gaining importance in the processing of chemical information an...

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the s...

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenom...

A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool.

STUDY OBJECTIVES: This study evaluated a novel artificial neural network (ANN) based sleep-disordere...

Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading caus...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

High-performance ligand-based virtual screening (VS) models have been developed using various comput...

Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks.

Mammography is the most popular technology used for the early detection of breast cancer. Manual cla...

Plasminogen activator inhibitor-1 is associated with the metabolism and development of advanced colonic polyps.

Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospe...

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.

To realize the full potential of deep learning for medical imaging, large annotated datasets are req...

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