Pang Xuejiao, Zhao Zijian, Weng Ying
School of Control Science and Engineering, Shandong University, Jinan 250061, China.
School of Computer Science, University of Nottingham, Nottingham NG7 2RD, UK.
Diagnostics (Basel). 2021 Apr 14;11(4):694. doi: 10.3390/diagnostics11040694.
At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.
目前，与传统机器学习相比，基于深度学习的人工智能（AI）在医学领域的应用变得更加广泛且适用于临床实践。将传统机器学习方法应用于临床实践极具挑战性，因为医学数据通常缺乏特征。然而，具有自学习能力的深度学习方法能够有效利用强大的计算能力来学习复杂且抽象的特征。因此，通过基于深度学习的计算机辅助诊断（CAD）系统，利用胃肠道内窥镜进行病变分类和检测，它们具有广阔前景。本研究旨在探讨基于深度学习的CAD系统的研究进展，以协助医生对胃、肠和食管中的病变进行分类和检测。研究还总结了当前方法的局限性，并最终对未来研究进行了展望。
