AIMC Topic: Adenoma
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Robotic Transanal Minimally Invasive Surgery (rTAMIS): Large Tubulovillous Adenoma.
The American surgeon
Feb 28, 2023
Many transanal platforms have recently evolved to manage rectal pathologies. Transanal endoscopic microsurgery (TEM) and transanal laparoscopic minimally invasive surgery (TAMIS) have been developed to address the limitations of conventional transana...
Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics.
Contrast media & molecular imaging
Feb 5, 2023
BACKGROUND: Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify ...
Polyp characterization using deep learning and a publicly accessible polyp video database.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Jan 18, 2023
OBJECTIVES: Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study deve...
Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.
Cancer cytopathology
Dec 16, 2022
BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-...
Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.
European journal of radiology
Dec 12, 2022
PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma.
Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.
Journal of gastroenterology
Aug 16, 2022
BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the ...
Automated histological classification for digital pathology images of colonoscopy specimen via deep learning.
Scientific reports
Jul 27, 2022
Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtaine...
Validation of a natural language processing algorithm to identify adenomas and measure adenoma detection rates across a health system: a population-level study.
Gastrointestinal endoscopy
Jul 16, 2022
BACKGROUND AND AIMS: Measuring adenoma detection rates (ADRs) at the population level is challenging because pathology reports are often reported in an unstructured format; further, there is significant variation in reporting methods across instituti...
Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.
Clinical neurology and neurosurgery
May 17, 2022
OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index...
A deep learning-based approach for the diagnosis of adrenal adenoma: a new trial using CT.
The British journal of radiology
May 9, 2022
OBJECTIVE: To develop and validate deep convolutional neural network (DCNN) models for the diagnosis of adrenal adenoma (AA) using CT.
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