![]() The accuracy of the system using HD-WLE is better than that of the general endoscope system. To solve those problems, Ebigbo et al established a CAD system based on deep learning algorithms. In addition, this system still has difficulty in identifying early cancerous lesions associated with Barrett's esophagus and selecting biopsy sites. This situation stimulated the development of a CAD system for early cancerous lesions in Barrett's esophagus based on a supervised ML blood learning algorithm. However, the improvement has not yet satisfied the endoscopists. High-definition WLE (HD-WLE) and the NBI endoscopy system were once considered to improve the accuracy of diagnosing early cancerous lesions associated with Barrett's esophagus. Role of AI in esophageal cancer detection 5.1 Barrett's dysplasia and early esophageal adenocarcinoma AI AI based on white light endoscopy (WLE) ) and NBI narrow-band endoscopic system: There are some limitations to the recognition of early cancerous lesions associated with Barrett's esophagus by WLE, a conventional technology. Esophageal adenocarcinoma is the most common pathology in Western countries, more than 40% of patients with esophageal adenocarcinoma are diagnosed after the disease has metastasized and the 5-year survival rate is less than 20. ![]() Esophageal cancers mainly include esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). Introduction to Esophageal Cancer Esophageal cancer (EC) is the eighth most common cancer and the sixth leading cause of cancer death worldwide. In predicting cancer risk, there is still a lack of prospective clinical studies to confirm the accuracy of the risk stratification model. In genomics, since genomic markers lack tissue specificity, they can now only be used as complementary measures. As AI techniques enter pathology, contour lesions that are difficult to identify by endoscopists may become easier than before. Many studies on complex neural networks in early esophageal cancer endoscopic image analysis show excellent performance including sensitivity and specificity and gradual progression from print image analysis in vitro for classification to real-time detection of esophageal tumors in practice. Deep learning (DL) has brought about breakthroughs in image, video and other aspects of processing, while complex neural networks (CNN) have paved the way for detect high-resolution endoscopic images and videos. Therefore, the demand for more effective methods of detecting early esophageal cancer features has led to intensive research in the field of artificial intelligence (AI). However, early endoscopic detection of esophageal cancer, especially Barrett's dysplasia or esophageal squamous dysplasia, is difficult. significant in improving the patient's prognosis. Overview of the application of artificial intelligence to gastrointestinal endoscopy Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of esophageal cancer is of great value. Deep Learning is a subset of Machine Learning, capable of being different in some important respects from traditional shallow Machine Learning, allowing computers to solve a series of complex problems that cannot be solved. In it, the computer groups similar data and pinpoints the anomaly. ![]() Machine Learning is often divided into supervised learning, where computers learn by example from labeled data, and unsupervised learning. Machine Learning is the process of teaching a computer to perform a task, instead of programming it how to perform that task step by step. Artificial Intelligence covers many areas of research, from genetic algorithms to expert systems, and provides scope for arguments about what constitutes artificial intelligence. What is the difference between artificial intelligence AI, Machine Learning and Deep Learning? Artificial intelligence is the study of how to build machines capable of performing tasks that normally require human intelligence. Artificial intelligence is a part of computer science and therefore it must be based on solid, applicable theories and principles of the field. 1.What is Artificial Intelligence? Artificial Intelligence (AI: Artificial Intelligence) can be defined as a branch of computer science that deals with the automation of intelligent behaviors.
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