Dermatology

Short-term effects of ocrelizumab on cortical pathology in patients with multiple sclerosis.

Cortical lesions (CLs) are a common finding in multiple sclerosis (MS) but data regarding the impact of treatment on their evolution are lacking. This study evaluates CLs status and new CLs accrual over two-year follow-up in a cohort of patients treated with ocrelizumab (OCR) and the correlation with brain structural metrics and clinical measures. 87 relapsing-remitting MS patients [59 (67.8%) wom...

Cross-modal attentive fusion network for tri-modal lesion growth prediction.

Pre-trained LSTM-RNN models with linear kernels fail to capture irregular, non-linear lesion growth patterns. In this paper presents a new deep learning model is called Cross-Modal Attention Fusion Network (CMAFN) to enhance an effective feature fusion of multiple medical image modalities such as Mammography, Magnetic Resonance Imaging (MRI) and Ultrasound (US). It enables progressively transforms...

LAM-CATNet: lambda-aware multi-scale cross-attention swin transformer network for mammogram classification.

The fundamental requirement for the initial recognition of a mammogram is the accurate segmentation and classification of breast lesions in mammograms...

Deep learning-based spatiotemporal estimation of lesion changes for patient-level assessment of breast cancer lung metastases on longitudinal CT.

Clinical management of breast cancer lung metastasis is challenging because of the complexity of dynamic lesion assessment. Traditional methods based ...

Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning.

BACKGROUND: Melanoma brain metastases treated with Gamma Knife radiosurgery show heterogeneous local control trajectories. Accurate prediction of time...

Machine learning models predict the immunotherapy response in tumors on the basis of DNA methylation.

BACKGROUND: The epigenetic control of immune responses plays a crucial role in the development and progression of cancer. The need to identify biomark...

Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.

PURPOSE: Accurate prediction of recurrence risk is crucial for personalizing therapy in human epidermal growth factor receptor 2-positive (HER2-positi...

Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy.

BACKGROUND AND PURPOSE: Radiation dermatitis (RD) and superficial soft tissue fibrosis are common toxicities among the patients with breast cancer rec...

Optimizing acquisition time and injected dose in 18F-FDG PET/CT imaging using deep learning: enhancing image protocol efficiency and safety.

BACKGROUND: This study aims to evaluate the effectiveness of deep learning algorithms in simulating standard acquisition time images from shortened ac...

A U-Net model for epidermal segmentation in optical coherence tomography images of actinic keratosis.

Actinic keratosis (AK) is a pre-cancerous skin lesion typically caused by excessive exposure to ultraviolet light. Optical coherence tomography (OCT) ...

A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images.

Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligen...

Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.

BACKGROUND: Gastric intestinal metaplasia (GIM) is often visually inconspicuous on routine endoscopy, while many artificial intelligence systems rely ...

Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.

Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. H...

Plasma signals of lung tumor promotion for molecular cancer prevention.

Predicting lung cancer risk would enhance prevention trials. Although the Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) trial demons...

Atopic dermatitis.

Atopic dermatitis (AD) is the most common inflammatory skin disease and carries the highest disability-adjusted life-years burden, ranking 15th among ...

Weakly-supervised deep learning on pathological whole-slide images for cutaneous vasculitis and its mimickers: a high-performance diagnostic support tool.

Cutaneous vasculitis represents a heterogeneous group of disorders characterized by inflammation and necrosis of blood vessels within the skin, with o...

Towards a clinically integrated artificial intelligence tool for triage of skin cancer.

Skin cancer is one of the most prevalent malignancies worldwide, with early detection critical to improving outcomes. However, in low-resource setting...

Correction: Q-CaDD: accelerating in silico methodologies with quantum computation and machine learning for Epidermal growth factor receptor.

Epigenetic reprogramming of tissue-resident memory T cells in chronic inflammatory disorders and implications for targeted therapies.

BACKGROUND: Tissue-resident memory T (TRM) cells play a role in causing long-term tissue injury in chronic inflammatory diseases via pathological epig...

A Data-Driven Normalization Framework for Subject-Specific Cerebrovascular Reactivity Assessment in Cerebrovascular Disease.

BACKGROUND: Assessment of cerebrovascular reactivity (CVR) has been reported using acetazolamide-augmented blood oxygenation level-dependent (BOLD) MR...