Pathology

AI for difficult herbarium specimens: identification of peat mosses (subgenus Sphagnum) without dissection.

Artificial intelligence (AI) for image-based herbarium specimen identification has thus far focused on plants that can be identified by eye. Here, we develop the first AI focused on identifying herbarium specimens of a bryophyte group, peat mosses in Sphagnum subgenus Sphagnum. These plants have substantial morphological plasticity, and confident identifications require time-consuming dissections ...

Hematopathology Practice in the Digital Era: What has Changed?

Hematopathology workflows are complex, since they include numerous data points necessary for guiding further testing, diagnosis, and patient management. The workflows start with complete blood cell counts, with subsequent morphologic evaluation of peripheral blood (PB) and bone marrow (BM). Digital pathology has the potential to revolutionize PB and BM assessment through the implementation of arti...

Real-time Use of Computer-aided Diagnosis in the Optical Diagnosis of Gastric Neoplasia: A Multicenter Randomized Controlled Trial.

BACKGROUND & AIMS: The multicenter, randomized, control trial was conducted to evaluate whether computer-aided diagnosis (CADx) improves the optical d...

Herbariograph: a deep-learning tool to classify specimen images.

Herbarium specimens are the physical evidence of plant diversity world-wide and a rich source of research data. With c. 402 M items, distributed in 3....

AI-based identification of Chinese vascular plants from herbarium specimens: a tool for all herbaria with Chinese holdings.

Documenting the geographic ranges of the World's > 350 000 named species of vascular plants requires artificial intelligence (AI) because there are in...

A two-step joint model based on deep learning realizes intelligent recognition of exfoliated cells in serous effusion.

Cytological examination of serous effusion is critical for diagnosing malignancies, yet it heavily relies on subjective interpretation by pathologists...

Machine learning pipeline with custom grid search for colorectal Raman spectroscopy data.

Colorectal cancer remains a major health burden, and its early detection is crucial for effective treatment. This study investigates the use of a hand...

A novel network for resolving subjective masking differences and accurate thyroid nodule diagnosis.

BACKGROUND: Over the past three decades, there has been a significant increase in the incidence of thyroid cancer. Ultrasound serves as a non-invasive...

Liquid biopsy in breast cancer management: From circulating biomarkers to clinical breakthroughs.

Breast cancer continues to be a major global health concern, particularly for women, despite improvements in early detection and treatment strategies....

A machine learning-driven transcriptomic study reveals the key role of Romo1 in reversing central sensitization through stellate ganglion block in migraine: An interventional study based on a recurrent migraine rat model.

OBJECTIVES/BACKGROUND: This study utilized a nitroglycerin (NTG)-induced chronic migraine rat model to explore the therapeutic effects and underlying ...

Repeatability and Reproducibility of Artificial Imaging-based Digital Pathology for the Evaluation of Liver Fibrosis.

BACKGROUND & AIMS: Fibrosis stage is a key determinant of outcomes in metabolic dysfunction-associated steatohepatitis (MASH). Assessment of fibrosis ...

Intraductal Papillary Mucinous Neoplasm and Pancreatic Cancer: Opportunity Knocks Twice.

Pancreatic cystic lesions are widely recognized as harbingers of pancreatic cancer. Intraductal papillary mucinous neoplasm (IPMN) is the most common ...

Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

OBJECTIVE: This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate can...

Integrating molecular generation and fingerprints transferring for single-molecule theranostics targeting endoplasmic reticulum stress.

INTRODUCTION: Precise diagnosis and treatment of diseases necessitate quantitative visualization and modulation of subcellular structures. The endopla...

MILK10k: A Hierarchical Multimodal Imaging-Learning Toolkit for Diagnosing Pigmented and Nonpigmented Skin Cancer and its Simulators.

In this study, we introduce MILK10k, a multimodal image dataset designed to enhance machine learning and artificial intelligence-driven applications f...

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognosticat...

Colon cancer survival prediction from gland shapes within histology slides using deep learning.

This study investigates the application of deep learning techniques for segmenting glands in histopathological images of colorectal cancer. We trained...

Diagnostic technologies for neuroblastoma.

Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an ...

B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights From Bioinformatics, Machine Learning, and Experimental Validation.

Glycosylation abnormalities are critical in the progression of various cancers. However, their role in the onset and prognosis of multiple myeloma (MM...

A comprehensive analysis of transcription factors identified TCF3 as a prognostic target for glioma.

Transcription factors (TFs) are pivotal in tumor initiation and progression, regulating downstream gene expression and modulating cellular processes. ...