Dermatology

Data-Driven Digital Twin for Amylosucrase from Deinococcus geothermalis: Accelerating Acceptor Discovery in Transglycosylation Reactions.

Enzymatic transglycosylation by Deinococcus geothermalis amylosucrase (DgAS) offers a cost-effective route for enhancing the physicochemical properties of bioactive flavonoids. However, predicting DgAS acceptor specificity remains challenging due to complex structure-activity relationships. Here, we developed a data-driven "digital twin" using deep learning-based Molecular Embeddings (MolE) and L1...

AI-Assisted Line-Field Confocal Optical Coherence Tomography to Detect Subclinical Basal Cell Carcinoma.

IMPORTANCE: Basal cell carcinoma (BCC) is the most common type of skin cancer worldwide. Particularly on the face, its locally destructive growth leads to functional and aesthetic impairment. Early diagnosis enables less invasive treatment. Line-field confocal optical coherence tomography (LC-OCT) is an effective noninvasive imaging method for the diagnosis of BCC. OBJECTIVES: To investigate wheth...

Renal Cell Carcinoma Texture Analysis With the KiTS23 Dataset: A Retrospective Study.

BACKGROUND AND AIMS: Computed tomography texture analysis, powered by machine learning techniques, may differentiate clear cell renal cell carcinoma (...

Prognostic value of body composition analysis in cancer patients treated with immunotherapy: a systematic evaluation of TotalSegmentator-based biomarkers.

PURPOSE: Body composition parameters offer objective, imaging-derived prognostic biomarkers, valuable in the context of oncological diseases. This stu...

Efficient Lesion Detection in Hysteroscopic Images Through Enhanced Attention Mechanisms and Directional Context Modeling.

Hysteroscopic lesion detection is clinically important for the early diagnosis of endometrial diseases. However, this task remains challenging due to ...

Cost-Effectiveness of an Artificial Intelligence as a Medical Device (AIaMD) for Triaging Patients Presenting to Primary Care with Concerns that They Have a Skin Cancer: A Modelling Study.

INTRODUCTION: Health services are struggling to cope with the growing numbers of people coming with skin lesions they are worried could be cancer. Usi...

Microfluidic Chip Platforms for Red Blood Cell Storage Lesion Quality Control and Precision Transfusion.

Red blood cell (RBC) transfusion is a core clinical intervention. However, hypothermic storage induces progressive biochemical, structural, and functi...

Evaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection.

OBJECTIVES: Computer-aided detection (CADe) using deep learning is promising for reducing missed gastric cancers (MGCs) and supporting physicians in d...

Deep-Learning Based Contrast Boosting: A Multi-Center Multi-Reader Study on Clinical Performance With Standard Contrast Enhanced Brain MRI.

BACKGROUND: Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. H...

SPCC: Inferring Spatial Cell-Cell Interaction by Integrating Single-cell and Spatial Transcriptomics.

Study of single-cell spatial biology reveals the importance of integrating single-cell and spatial data for capturing spatial structure at individual ...

Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation.

Prostate magnetic resonance imaging (MRI) reporting is a high-impact communication task because small differences in lesion laterality, sector localiz...

A comprehensive framework for multi-class prostate cancer classification using biparametric MRI: a multi-center retrospective study.

OBJECTIVES: Accurate pre-biopsy or preoperative diagnosis and risk stratification of prostate cancer remain challenging. This study aimed to develop a...

Alopecia Areata: Advances in Clinical Evaluation and Pathogenesis.

Alopecia areata (AA) is a common autoimmune condition that affects approximately 2% of the global population. Although it most commonly presents with ...

Deep Learning-Accelerated 3D Vessel Wall MRI: Prospective Evaluation of Sonic DL with Substantial Scan Time Reduction.

PURPOSE: While 3D vessel wall MRI (VW-MRI) is essential for characterizing vasculopathies, prolonged acquisition times limit its utility by increasing...

Clustering analysis of hamstring injuries-A pilot study to determine phenogroups based on injury characteristics and recovery duration.

PURPOSE: The objective of this pilot study was to determine clinically relevant phenogroups of professional football players with hamstring injuries u...

Transformer-Based Deep Learning Framework for Automated Lesion Detection in Capsule Endoscopy: A Comparative Study With CNN Architectures.

GOALS: To compare a vision transformer with 2 convolutional neural network architectures for multiclass lesion classification in capsule endoscopy ima...

Topical exposure to di(2-ethylhexyl) phthalate aggravates atopic dermatitis: an integrated network toxicology, bioinformatics, and experiment analysis.

Di(2-ethylhexyl) phthalate (DEHP) is one of the most widely used plasticizers; however, whether cutaneous DEHP exposure affects atopic dermatitis (AD)...

Emerging therapies against infections due to free-living amoebae.

INTRODUCTION: Pathogenic free-living amoebae, including Naegleria fowleri, Acanthamoeba spp. and Balamuthia mandrillaris, are rare but frequently fata...

Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status.

Keratinocyte carcinoma (KC) places a considerable and growing burden on healthcare systems. Given the KC population's heterogeneity, tailored clinical...

AI Simplification of Dermatopathology Reports for Patients: Basic Versus Prompt-Engineered Approaches.

BACKGROUND: Patients struggle to comprehend dermatopathology reports. As artificial intelligence (AI) tools become more accessible, patients may use t...