Dermatology

Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection.

AIMS: This study aimed to examine the impact of deep-learning reconstruction (DLR) on zero echo time...

Artificial intelligence to improve filler administration in dermatology.

Asymmetric lesion detection with geometric patterns and CNN-SVM classification.

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked...

Efficient multi-stage feedback attention for diverse lesion in cancer image segmentation.

In the domain of Computer-Aided Diagnosis (CAD) systems, the accurate identification of cancer lesio...

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breas...

Appraisal of AI-generated dermatology literature reviews.

BACKGROUND: Artificial intelligence (AI) tools have the potential to revolutionize many facets of me...

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor p...

Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis.

BACKGROUND: Over the past decade, several studies have shown that potential of artificial intelligen...

Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.

Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to pre...

GREnet: Gradually REcurrent Network With Curriculum Learning for 2-D Medical Image Segmentation.

Medical image segmentation is a vital stage in medical image analysis. Numerous deep-learning method...

ELA-Net: An Efficient Lightweight Attention Network for Skin Lesion Segmentation.

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivot...

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis.

BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognit...

Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI.

Machine learning offers great potential for automated prediction of post-stroke symptoms and their r...

Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin-eosin-stained tissue slides.

AIMS: Uveal melanoma has a high propensity to metastasize. Prognosis is associated with specific dri...

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images.

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) o...

GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images.

Automatic mucosal lesion segmentation is a critical component in computer-aided clinical support sys...

Deep Deblurring in Teledermatology: Deep Learning Models Restore the Accuracy of Blurry Images' Classification.

Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep...

Underserved populations and health equity in dermatology: Digital medicine and the role of artificial intelligence.

We have reviewed the current literature focused on the role of artificial intelligence (AI) for unde...

Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans.

The integration of teledermatology and artificial intelligence (AI) marks a significant advancement ...

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice io...

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection.

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with...