Pathology

Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition.

Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phylogenetically related species due to their morphological similarities. This study presents FungalNet, a deep learning model integrating ResNet-50 architecture with Focal ...

Relative Aortic Valve Stiffness: A New Parameter Quantifying Aortic Stenosis.

Background: Aortic stenosis (AS) quantification relies on transvalvular gradients or aortic valve opening area (AVA), two measures that inherently depend on the instantaneous transvalvular flow rate (Q). We present a new quantification method for AS based on new modelling where AVA saturates with increasing Q, contradicting the assumption of linear Q-AVA-relation currently predominant in the liter...

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.

OBJECTIVES: Multidisciplinary tumor boards (MDTs) are critical for the personalized management of soft tissue sarcomas (STS), but they are limited by ...

Integrative transcriptomic analysis identifies synovium-derived biomarkers for OA-related synovitis and builds a validated diagnostic nomogram.

This study identifies diagnostic biomarkers of OA-related synovitis from synovial tissue expression and develops a validated diagnostic nomogram (diff...

Multicenter clinicopathological study of odontogenic myxoma spectrum lesions using quantitative pathology.

Odontogenic myxoma (OM) and odontogenic myxofibroma (OMF) are rare benign odontogenic tumors characterized by heterogeneous stromal composition, for w...

Comparative analysis of large language models as decision support tools in oral pathology.

This study evaluated the performance of four large language model based chatbots (LLMs) (ChatGPT-4.0, ChatGPT o1-preview, Gemini, and Meta AI) as deci...

Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.

Immune checkpoint inhibitors (ICIs) have transformed the advanced melanoma treatment landscape; however, a subset of patients achieve durable response...

Benchmarking spectroscopic-chemometric models for human bloodstain time since deposition in tropical outdoor microenvironments.

Reliable estimation of the time since deposition (TSD) of bloodstains remains a key challenge in forensic reconstruction, particularly under realistic...

Precise, fast, and automated gel quantification powered by YOLO11 instance segmentation.

BACKGROUND: Gel electrophoresis is a cornerstone technique in analytical chemistry and proteomics, yet downstream image analysis remains a bottleneck ...

PH2ST: Prompt-guided hypergraph learning for spatial transcriptomics prediction in whole slide images.

Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and ...

Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction.

Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. Howev...

Improved sub-visible particle classification in flow imaging microscopy via generative AI-based image synthesis.

Sub-visible particle analysis using flow imaging microscopy combined with deep learning has proven effective in identifying particle types, enabling t...

Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks - Foundations and Emerging Opportunities in Spatial Pathobiology.

Cytology cell block specimens are essential diagnostic materials in patients with advanced-stage malignancy and often represent the only available sub...

Signature-aware deep learning reveals distinct driver gene programs and mutational processes in glioblastoma and colon adenocarcinoma.

The precise identification of cancer driver mutations is essential for precision oncology; however, it remains a significant challenge because of the ...

Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.

Current gastric cancer (GCa) risk systems are prone to errors since they evaluate a visual estimation of intestinal metaplasia percentages in histopat...

CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.

Cystoscopy is a common endoscopic procedure used for the visual inspection of the lower urinary tract, particularly for the detection and surveillance...

Quantitative 3D Real-Space Analysis of Photonic Supraparticles.

Supraparticles (SPs) are assemblies of smaller particles, and they form an interesting material class. One way through which these structures can be f...

Recent Advances in The Virtual Fields Method for Evaluating and Identifying Tissue Biomechanical Properties and Constitutive Parameters.

The derivation of material parameters for soft tissues has recently been transformed by advances in full-field measurement techniques providing high-r...

Evaluating deep learning models for pancreatic cancer diagnosis.

Pancreatic cancer is a highly aggressive and often fatal disease, with early detection being a key factor for improving patient survival. Recent advan...

Research progress of machine learning applications in gastric cancer diagnosis and therapy.

Gastric cancer (GC), a malignant neoplasm originating from the gastric mucosal epithelium, represents one of the most prevalent cancers worldwide. Ear...