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Adenoma

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Current status and limitations of artificial intelligence in colonoscopy.

United European gastroenterology journal
BACKGROUND: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further effor...

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network.

Sensors (Basel, Switzerland)
A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, ...

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions.

Magnetic resonance imaging
PURPOSE: To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.

Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study.

The American surgeon
BACKGROUND: Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning ...

Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting.

Radiology
Background Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning-based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, whic...

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

BMJ open
OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathol...

Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.

Computational and mathematical methods in medicine
METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model is used to detect whether the image contains lesions: CRC, colorectal adenoma (CRA), and colorectal...

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.

Neuroradiology
PURPOSE: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could he...

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build...