Pathology

Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study.

PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially available large language models (LLM), ChatGPT-4o and ChatGPT-5, in cervical dysplasia management. METHODS: Twenty-three anonymized real-life patient cases with multiple-choice (MC) questions regarding treatment decisions were used to assess answer qualit...

Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.

OBJECTIVES: To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detected cancers. MATERIALS AND METHODS: This retrospective study was based on data from 130,031 screening examinations performed in BreastScreen Norway (2008-2018), processed by two AI models: a commercial model (A) and an in-house model (B). The study sa...

Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.

BACKGROUND: Breast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent ...

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics.

Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips...

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell-Cell Interaction Inference.

Spatial transcriptomics (ST) enables the study of tissue architecture by resolving gene expression in space, but current ST platforms are constrained ...

Deep Learning of CT Imaging Predicts PD-L1 Expression and Immunotherapy Response in Metastatic NSCLC: A Multi-Center Study.

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Deep Learning vs. Traditional Machine Learning in the Diagnosis of Cutaneous Drug Eruptions: A Comparative Study with Explainable AI on Whole-Slide Images.

Histopathological diagnosis of cutaneous drug eruptions (CDEs) presents a challenge in dermatopathology due to morphological overlap and high inter-ob...

UNICORN: a deep learning model for integrating multi-stain data in histopathology.

The integration of multi-stain histopathology images through deep learning poses a significant challenge. Current approaches struggle with data hetero...

Multifaceted role of histopathology in liver transplantation for hepatocellular carcinoma: A narrative review towards precision prognosis.

Hepatocellular carcinoma is a leading cause of cancer mortality globally. Liver transplantation is considered the best curative treatment for selected...

An in situ non-contact 3D microscopic measurement system for temporal bone anatomy based on stereo imaging.

Accurate quantitative assessment of temporal bone microanatomy is essential for otologic research and surgical planning. However, existing measurement...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer.

Data scarcity, inter-institutional stain variability, and privacy constraints are major challenges impeding the development of generalizable artificia...

Identifying breast cancer subtypes and exploring spatial expression patterns of the key subtype-specific gene based on pathological images, single-cell sequencing, and spatial transcriptomic data.

BACKGROUND: Breast cancer (BRCA) is a heterogeneous disease. Accurate prognosis and molecular subtypes are critical for personalized treatment in BRCA...

Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.

Cancers of unknown primary (CUP) refer to a highly heterogeneous group of metastatic tumors whose primary site remains undetectable despite comprehens...

Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.

BACKGROUND: Real-time endoscopic diagnosis of Helicobacter pylori infection remains challenging and often requires biopsy-based testing, delaying trea...

Identification of potential biomarkers for ulcerative colitis based on multidimensional data analysis and investigation of the mechanisms of therapeutic effects.

OBJECTIVE: Based on multidimensional data analysis, potential biomarkers for ulcerative colitis were screened, and the effects of curcumin chitosan mi...

A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.

PURPOSE: The aim of this study is to develop a deep learning model using preoperative multimodal MR data to predict the Ki-67 expression level of glio...

Intelligent three-dimensional imaging in pathology: Applications and developments.

Conventional two-dimensional (2D) pathology relies on a limited number of tissue sections and therefore provides information from isolated planes, whi...

External Validation of a Deep Learning-Based Artificial Intelligence System for Ultrasound Diagnosis of Thyroid Nodules: A Two-Center Retrospective Study.

OBJECTIVES: To investigate the performance of an artificial intelligence (AI) diagnostic system for thyroid nodule sonography based on deep learning c...