AIMC Topic: Biopsy

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Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.

Journal of radiation research
This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results on whole slide images (WSIs), and explore the clinical significance of obtained features. This study...

Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study.

World journal of gastroenterology
BACKGROUND: The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis.

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.

Radiology. Artificial intelligence
Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated...

Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Medicine
Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires c...

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small s...

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

JNCI cancer spectrum
BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided...

A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases.

The British journal of dermatology
BACKGROUND: Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning (DL) to enhance disease management. Dermatology has seen improved diagnostics, particularly in skin cancer detection, through the integration of AI. How...

A Deep Learning-Based Approach to Estimate Paneth Cell Granule Area in Celiac Disease.

Archives of pathology & laboratory medicine
CONTEXT.—: Changes in Paneth cell numbers can be associated with chronic inflammatory diseases of the gastrointestinal tract. So far, no consensus has been achieved on the number of Paneth cells and their relevance to celiac disease (CD).

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Clinical and experimental dermatology
Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of...