AIMC Topic: Biopsy
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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
Oct 16, 2024
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...
Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.
Veterinary pathology
Oct 14, 2024
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...
Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.
Journal of the American Society of Nephrology : JASN
Oct 9, 2024
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...
A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.
Journal of dental research
Oct 9, 2024
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...
A deep learning approach to case prioritisation of colorectal biopsies.
Histopathology
Oct 3, 2024
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).
Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.
Minerva dental and oral science
Sep 27, 2024
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...
Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence.
Journal of pediatric gastroenterology and nutrition
Aug 9, 2024
OBJECTIVES: Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rectal biopsy, which reveals the aganglionosis and the presence of cholinergic hyperinnervation. However...
Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.
Asian Pacific journal of cancer prevention : APJCP
Aug 1, 2024
OBJECTIVE: To summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC).
A decision support system for the detection of cutaneous fungal infections using artificial intelligence.
Pathology, research and practice
Jul 21, 2024
Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. ...
Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.
Frontiers in immunology
Jul 5, 2024
BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning sys...
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