AIMC Topic: Precancerous Conditions

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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...
Adenoma Colonoscopy Light Deep Learning Colonic Polyps Precancerous Conditions Computational Biology Image Interpretation, Computer-Assisted Intestinal Polyps Mass Screening Humans Neural Networks, Computer Colorectal Neoplasms Diagnostic Errors
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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...
Humans Artificial Intelligence Neoplasm Staging Neoplasm Grading Disease Management Precancerous Conditions Disease Susceptibility Algorithms Deep Learning Head and Neck Neoplasms Databases, Genetic
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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...
Precancerous Conditions Pharyngeal Neoplasms Esophageal Neoplasms Colonic Polyps Image Interpretation, Computer-Assisted Capsule Endoscopy Stomach Neoplasms Colorectal Neoplasms Deep Learning Rectal Diseases Endoscopy, Gastrointestinal Helicobacter Infections Humans Helicobacter pylori Gastrointestinal Neoplasms
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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.
Humans Male Middle Aged Lung Diagnosis, Computer-Assisted Neural Networks, Computer Retrospective Studies Female Aged Radiographic Image Interpretation, Computer-Assisted Precancerous Conditions Radiography, Thoracic Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Radiography
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Two dimensional multifractal detrended fluctuation analysis of low coherence images for diagnosis of cervical pre-cancer.

Biomedical physics & engineering express Feb 18, 2020
We report detection of cervical pre-cancer through their low coherence images by applying two dimensional multifractal detrended fluctuation analysis. Low coherent backscattered images of pre-cancerous cervical tissue sections were captured using a c...
Precancerous Conditions Uterine Cervical Dysplasia Fractals Female Uterine Cervical Neoplasms Image Processing, Computer-Assisted Support Vector Machine Humans Algorithms
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Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 24, 2020
Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dyspla...
Precancerous Conditions Early Detection of Cancer Esophageal Neoplasms Barrett Esophagus Image Enhancement Humans Deep Learning Microscopy, Confocal
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A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology Jan 4, 2020
BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, ...
Computer Simulation Risk Assessment Early Detection of Cancer Internet Mouth Neoplasms Precancerous Conditions Humans Machine Learning Software
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Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.

Cancer letters Oct 17, 2019
Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from benign to malignant disease. Precise and timely management of malignant pancreatic cysts might prevent t...
Prognosis Support Vector Machine Pancreatic Neoplasms Pancreas Humans Artificial Intelligence Precancerous Conditions Machine Learning Radiometry Precision Medicine Pancreatic Cyst
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Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

Gastrointestinal endoscopy Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Esophagus Esophageal Neoplasms Observer Variation Precancerous Conditions Optical Imaging Esophageal Squamous Cell Carcinoma Esophageal Diseases Narrow Band Imaging Middle Aged Deep Learning Image Processing, Computer-Assisted Neoplasm Invasiveness Aged, 80 and over Adult Neural Networks, Computer Sensitivity and Specificity Female Male Reproducibility of Results Aged Humans
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Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos).

Gastrointestinal endoscopy Aug 21, 2019
BACKGROUND AND AIMS: We developed a system for computer-assisted diagnosis (CAD) for real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinomas (ESCCs) to assist the diagnosis of esophageal cancer.
Middle Aged Young Adult Esophageal Neoplasms Diagnosis, Computer-Assisted Humans Precancerous Conditions Adult Deep Learning Retrospective Studies Esophageal Squamous Cell Carcinoma Sensitivity and Specificity Adolescent Female Narrow Band Imaging Male Aged
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