AIMC Topic: Precancerous Conditions
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Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.
Journal of healthcare engineering
Aug 17, 2021
Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient's survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appe...
Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study.
Clinical and translational gastroenterology
Aug 3, 2021
INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evalu...
Artificial intelligence for the detection of gastric precancerous conditions using image-enhanced endoscopy: What kind of abilities are required for application in real-world clinical practice?
Gastrointestinal endoscopy
Jun 25, 2021
Current status and limitations of artificial intelligence in colonoscopy.
United European gastroenterology journal
Jun 7, 2021
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...
AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation.
British journal of cancer
Mar 19, 2021
Smartphone applications ("apps") with artificial intelligence (AI) algorithms are increasingly used in healthcare. Widespread adoption of these apps must be supported by a robust evidence-base and app manufacturers' claims appropriately regulated. Cu...
Skin Neoplasms
Adult
Sensitivity and Specificity
Risk Assessment
Humans
Artificial Intelligence
Algorithms
Europe
United States Food and Drug Administration
Precancerous Conditions
Evidence-Based Medicine
Smartphone
Mobile Applications
United States
Early Detection of Cancer
European Union
Diagnostic Test Approval
Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.
Radiology
Feb 23, 2021
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonograph...
Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.
Computational and mathematical methods in medicine
Aug 18, 2020
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...
Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.
Oral oncology
Jul 13, 2020
This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slid...
Utilizing artificial intelligence in endoscopy: a clinician's guide.
Expert review of gastroenterology & hepatology
Jun 17, 2020
INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the...
Humans
Stomach Neoplasms
Colonic Polyps
Pharyngeal Neoplasms
Helicobacter Infections
Image Interpretation, Computer-Assisted
Colorectal Neoplasms
Helicobacter pylori
Capsule Endoscopy
Deep Learning
Gastrointestinal Neoplasms
Precancerous Conditions
Rectal Diseases
Esophageal Neoplasms
Endoscopy, Gastrointestinal
Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
European radiology
Apr 29, 2020
OBJECTIVES: To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs.
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