Dermatology

Circulating MicroRNA Signatures in Severe Alopecia Areata: Diagnostic Discrimination, Pathway Analysis, and Therapeutic Implications.

INTRODUCTION: Alopecia areata (AA) is an autoimmune disorder characterized by non-scarring hair loss due to immune dysregulation. Despite advances, its precise molecular mechanisms remain unclear. This study investigates plasma microRNA (miRNA) expression profiles in patients with AA to identify biological pathways influenced by miRNAs and potential therapeutic targets. METHODS: A total of 50 pati...

Spatial lipidomics-based melanoma diagnostics by integration of mass spectrometry imaging and machine learning.

Melanoma is an aggressive skin cancer with a rising global incidence, yet its lipidomic alterations remain understudied. We applied desorption electrospray ionization mass spectrometry imaging to 137 frozen skin specimens (65 paired normal, 57 melanomas, 15 melanocytic nevi) and developed a lipidomics-based machine learning model for diagnostic evaluation. At the clinically relevant lesion level, ...

mRNA-based cancer vaccines: A new frontier in personalized immunotherapy.

The advancement of mRNA technology has rejuvenated the cancer treatment immunotherapy field by providing a flexible and scalable platform to generate ...

Independent evaluation of deep learning models for detecting focal cortical dysplasia.

The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) ...

Thymic health and immunotherapy outcomes in patients with cancer.

Although immunotherapy has revolutionized cancer treatment, many patients still experience limited benefit, highlighting the urgent need for improved ...

Characterizing mating behaviour in broiler breeders via a vision based deep learning model.

Broiler breeder mating behaviour consists of rapidly performed sub-actions that determine the success of sperm transfer critical for egg fertility. It...

Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data.

Reducing scan times, radiation dose, and enhancing image quality, especially for lower-performance scanners, are critical in low-count/low-dose PET im...

A multi-focus oral panoramic x-ray image dataset based on pixel-level annotations.

The dataset is a comprehensive, large-scale collection of panoramic X-ray images developed to advance research in dental artificial intelligence. It c...

A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans.

Lung cancer is an extremely fatal kind of cancer, resulting in the deaths of almost 7.6 million individuals annually around the globe. Nevertheless, a...

AI-enhanced precision: boosting pathologist confidence in HER2 ultra-low diagnosis through intelligent re-screening.

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) immunohistochemistry (IHC) expression, particularly the precise identification ...

A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.

Cutaneous leishmaniasis (CL) is a neglected tropical and zoonotic disease affecting both human and animal health, for which microscopic examination of...

Twisted Tapestry of Tumors: Updates in Molecular Pathology of Breast Tumors.

This article explores the expanding role of molecular diagnostics in breast pathology. It emphasizes how immunohistochemistry, fluorescence in situ hy...

Identified BIRC5 and HDAC1 as novel diagnostic biomarkers linked to centrosome-immune crosstalk for cutaneous squamous cell carcinoma via machine learning-based multi-omics analysis.

Generating synthetic CEM from low-energy images using deep learning: A future without contrast media? A proof-of-concept study.

OBJECTIVE: We used deep learning to generate synthetic, resembling in appearance, iodine-enhanced, mammograms from low-energy contrast-enhanced mammog...

A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy.

INTRODUCTION: Digestive endoscopy is a critical modality for diagnosing and managing gastrointestinal diseases, yet it faces challenges including oper...

Deep learning synthesis of virtual T2-weighted fat-suppressed MR images: a multi-center study.

OBJECTIVES: To develop a Generative Adversarial Network (GAN) for generating virtual T2 fat-suppressed (T2FS) sequences from standard T1- and T2-weigh...

Study of bladder cancer detection in standard white light versus AI-supported endoscopy-01 (RAISE-01) - Development and validation of an AI-based support tool.

BACKGROUND: Diagnosis and surveillance of bladder cancer rely on white-light cystoscopy (WLC). However, this modality is operator-dependent and associ...

The promise of biomarkers in Psoriatic Arthritis: Moving towards precision medicine.

Psoriatic arthritis is a heterogeneous disease where delayed diagnosis and variable treatment responses remain significant challenges. This review add...

Assessing the robustness of AI lesion risk scores at different exposure settings using an anthropomorphic breast phantom.

To assess the robustness of risk scores provided by an artificial intelligence (AI) system for digital mammography (DM), when varying the exposure con...

Annotation and characterization of lesions in breast tomosynthesis images.

Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for ...