Latest AI and machine learning research in pathology for healthcare professionals.
The assessment of left ventricular diastolic function started with invasive pressure measurements and is currently primarily based on echocardiographic imaging. The current approach to the diagnosis of diastolic function relies on mitral inflow velocities, tissue Doppler early diastolic velocity of the mitral annulus, peak velocity of tricuspid regurgitation, pulmonary vein flow, left atrial size ...
Efforts to create rapid, non-invasive, and reliable cancer diagnostics have increasingly focused on extracellular vesicles (EVs), nanoscale carriers of proteins, lipids, and nucleic acids that mirror the molecular state of their parent cells and mediate communication within the tumor microenvironment. Their complex composition and heterogeneity present however, significant challenges for analytica...
PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially avail...
OBJECTIVES: To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detect...
BACKGROUND: Breast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent ...
Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips...
Spatial transcriptomics (ST) enables the study of tissue architecture by resolving gene expression in space, but current ST platforms are constrained ...
Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies...
Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...
Histopathological diagnosis of cutaneous drug eruptions (CDEs) presents a challenge in dermatopathology due to morphological overlap and high inter-ob...
The integration of multi-stain histopathology images through deep learning poses a significant challenge. Current approaches struggle with data hetero...
Hepatocellular carcinoma is a leading cause of cancer mortality globally. Liver transplantation is considered the best curative treatment for selected...
Accurate quantitative assessment of temporal bone microanatomy is essential for otologic research and surgical planning. However, existing measurement...
PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...
Data scarcity, inter-institutional stain variability, and privacy constraints are major challenges impeding the development of generalizable artificia...
BACKGROUND: Breast cancer (BRCA) is a heterogeneous disease. Accurate prognosis and molecular subtypes are critical for personalized treatment in BRCA...
Cancers of unknown primary (CUP) refer to a highly heterogeneous group of metastatic tumors whose primary site remains undetectable despite comprehens...
BACKGROUND: Real-time endoscopic diagnosis of Helicobacter pylori infection remains challenging and often requires biopsy-based testing, delaying trea...
OBJECTIVE: Based on multidimensional data analysis, potential biomarkers for ulcerative colitis were screened, and the effects of curcumin chitosan mi...
Conventional two-dimensional (2D) pathology relies on a limited number of tissue sections and therefore provides information from isolated planes, whi...