Pathology

Machine learning-based discrimination of benign and malignant breast lesions on US: The contribution of shear-wave elastography.

PURPOSE: To build and validate a combined radiomics and machine learning (ML) approach using B-mode US and SWE images to differentiate benign from malignant solid breast lesions (BLs) and compare its performance with that of an expert radiologist.

Rapid On-Site Histology of Lung and Pleural Biopsies Using Higher Harmonic Generation Microscopy and Artificial Intelligence Analysis.

Lung cancer is one of the most prevalent and lethal cancers. To improve health outcomes while reducing health care burden, it becomes crucial to move toward early detection and cost-effective workflows. Currently, there is no method for the on-site rapid histologic feedback on biopsies taken in diagnostic, endoscopic, or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-b...

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity ...

Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images.

The purpose of this study was to evaluate the performance of convolutional neural network (CNN)-based image segmentation models for segmentation and c...

Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.

OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool ...

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently ...

BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models.

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from th...

Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.

Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histol...

Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial inf...

The Future of Oral Oncology: How Artificial Intelligence is Redefining Surgical Procedures and Patient Management.

INTRODUCTION AND AIMS: The future of oral oncology is significantly influenced by the incorporation of artificial intelligence (AI) technologies, such...

Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with m...

Triple and quadruple optimization for feature selection in cancer biomarker discovery.

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on ...

Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.

The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR...

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorizatio...

Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.

DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for e...

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensive...

Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.

Based on bioinformatics and machine learning methods, we conducted a study to screen the core genes of minimal change disease (MCD) and further explor...

Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort.

PURPOSE: There are few markers to identify those likely to recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG). We deve...

A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy.

PURPOSE: Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review...

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method.

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on ...