Pathology

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise and costly genetic tests. To overcome these barriers, we developed a computational pipeline leveraging deep learning methods to accurately classify pediatric sarcoma subtypes from digitized histology slides. To ensure classifier generalizability and...

Fuzzy-logic-driven predictions of multi-tissue ingrowth for rapid screening of mechanically instructive tissue-engineering scaffolds.

High retear rates in surgical intervention of interfacial/transitional tissues (bone-tendon, bone-ligament) drive a need to design tissue engineering scaffolds that can successfully mimic healthy tissue mechanics. Current testing mechanisms of scaffolds depend on bioreactor systems or animal models of damaged tissue. While valuable, these methods present bottlenecks in time and cost to determine p...

Malignant vs. Non-malignant Annotations on TCGA Breast Cancer Whole Slide Images for AI Analysis.

Identification of malignant and non-malignant regions in breast cancer whole slide images (WSIs) is essential for understanding tumor heterogeneity an...

Photon-counting Detector CT Spectral Reconstructions for Radiomics-based Liver Lesion Classification: A Multicenter Study.

OBJECTIVES: Accurate noninvasive classification of hepatic lesions remains a diagnostic challenge, particularly on conventional CT. Photon Counting De...

Utilizing natural language processing (NLP) to identify breast cancer-associated lung metastases from pathology reports to delineate characteristics of this site of recurrence.

PURPOSE: Natural language processing (NLP, artificial intelligence) can enable automated identification of records in large datasets. The purpose of t...

Atomic-resolution imaging of gold species at organic liquid-solid interfaces.

The structure and dynamics of adsorbed atoms (adatoms) at solid-liquid interfaces determine the performance of advanced catalysts, electrochemical dev...

Multimodal AI model for early detection of hepatocellular carcinoma.

Detection of early hepatocellular carcinoma (eHCC) is important for timely treatment and improved prognosis. However, it is challenging to distinguish...

Automated Detection and Classification of Pleural Effusion on Computed Tomography Using Deep Learning.

The aim of this study is to develop and evaluate the performance of a two-stage deep learning-based artificial intelligence framework for the automati...

Can large language models like ChatGPT and Gemini interpret cervical cytology accurately?

Large language models (LLMs) have shown promise in medical imaging, but their utility in cytology remains underexplored. This study evaluates GPT-5 an...

Automated Detection and Classification of Radiology Report Discrepancies Using NLP: A Tool for Resident Education and Quality Assurance.

PURPOSE: The aim of this study was to develop and evaluate a natural language processing (NLP) system that automatically detects and classifies discre...

Towards generalizable pathology reports via a multimodal LLM with the multicenter in-context learning.

Pathology report generation has received increasing attention in recent years. However, existing pathology report generation methods still face two ma...

LCPBert: ProtBERT-based early-stage lung cancer prediction from T cell receptor beta sequences.

Early detection of lung cancer remains challenging due to limitations of current methods. We developed LCPBert, a deep learning framework leveraging p...

Ultrasonographic Evaluation of Pediatric Thyroid Nodules: Adult Risk Stratification Systems, 2021 K-TIRADS Revision, and Future Refinements.

Although pediatric thyroid cancer is rare, it has characteristics distinct from those of adult thyroid cancer. Thyroid nodules in children present a h...

Bridging electron microscopy and materials analysis with an autonomous agentic platform.

Electron microscopy (EM) reveals atomic-scale structures that underpin catalysis, energy storage, and semiconductor reliability, yet current workflows...

Artificial Intelligence-Based Exosome Analysis for Improving Diagnostic Performance of Breast Lesions on Ultrasound: Protocol of a Prospective, Multicenter Cohort Study.

PURPOSE: Exosome-surface enhanced Raman spectroscopy-artificial intelligence platform (exosome-SERS-AI) is an innovative liquid biopsy method that acq...

Deep learning-based image super-resolution in microscopy: Why more pixels do not imply higher resolving ability?

Deep learning-based image super-resolution is a popular topic in computer vision and artificial intelligence (AI)-based imaging. A survey of literatur...

Factors Influencing Universal Coverage of AI-Assisted Cervical Cancer Screening: Qualitative Study Based on the Macro Model of Health System.

BACKGROUND: Improving screening coverage is a central goal of the global strategy to eliminate cervical cancer. In resource-constrained settings, insu...

Evolution of the Diagnostic Paradigm for Giant Cell Arteritis: From Histopathology to Multi-Modal Imaging Integration Running title: GCA: Histology to Imaging.

Giant cell arteritis (GCA) is a systemic vasculitis that predominantly affects mediumand large-sized arteries. Delayed diagnosis may result in irrever...

Oxidative stress reprograms benign prostatic hyperplasia microenvironments: insights from integrative multi-omics and machine learning.

BACKGROUND: Oxidative stress (OS) is increasingly implicated in benign prostatic hyperplasia (BPH), yet the underlying cellular programs remain unclea...

Enhancing AI's ability to interpret histological images with prompt engineering: An evaluation of GPT-4o performance.

This study examines the effect of various prompting strategies on ChatGPT's ability to interpret histological images across different tissue types and...