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Biopsy

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Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.

Minerva dental and oral science
BACKGROUND: The study's objective was to develop an automated method for a histopathology recognition model for odontogenic keratocysts (OKC) and non-keratocyst (Non-KC) in jaw cyst sections stained with hematoxylin (H) and eosin (E) on a tiny bit of...

Deep Learning-Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study.

The Journal of investigative dermatology
The diagnosis of early-stage mycosis fungoides (MF) is challenging owing to shared clinical and histopathological features with benign inflammatory dermatoses. Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classi...

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Nature medicine
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endp...

A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.

Journal of dental research
Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investig...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Obesity surgery
PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver...

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

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
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 r...

A deep learning approach to case prioritisation of colorectal biopsies.

Histopathology
AIMS: To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis).

Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Journal of clinical pathology
Microscopic review of tissue sections is of foundational importance in pathology, yet the traditional chemistry-based histology laboratory methods are labour intensive, tissue destructive, poorly scalable to the evolving needs of precision medicine a...

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

Veterinary pathology
Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for...