Primary Care

Smoking & Tobacco

Latest AI and machine learning research in smoking & tobacco for healthcare professionals.

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Showing 169-189 of 3,112 articles
Patterns of Marijuana Use and Nicotine Exposure in Patients Seeking Elective Aesthetic Procedures.

BACKGROUND: With the increasing legalization and popularity of marijuana, it is frequently and somet...

Combination of headspace single-drop microextraction (HS-SDME) with a nickel-embedded paper-based analytical device for cyanide quantification.

BACKGROUND: Cyanide anion can be found in foodstuffs, tobacco smoke and a variety of types of waters...

Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.

 Although abundant literature is currently available on the use of deep learning for breast cancer ...

Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability.

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare...

Neuron Contact Detection Based on Pipette Precise Positioning for Robotic Brain-Slice Patch Clamps.

A patch clamp is the "gold standard" method for studying ion-channel biophysics and pharmacology. Du...

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring.

Respiratory pattern is one of the most crucial indicators for accessing human health, but there has ...

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity.

Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in ...

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images.

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as...

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagn...

Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach.

The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) ...

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of signif...

Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation.

Pixel-level annotations are extremely expensive for medical image segmentation tasks as both experti...

Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism.

The employment of machine learning approaches has shown promising results in predicting cancer. In t...

Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients.

To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditi...

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard...

Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of w...

Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models.

Image restoration under adverse weather conditions has been of significant interest for various comp...

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation.

The widespread application of artificial intelligence in health research is currently hampered by li...

Gigapixel end-to-end training using streaming and attention.

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel ...

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