| Literature DB >> 28915974 |
Jing Zhang1, Yanlin Song2, Fan Xia2, Chenjing Zhu2, Yingying Zhang2, Wenpeng Song2, Jianguo Xu3, Xuelei Ma4.
Abstract
Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm. Large size of the training dataset is critical to increase the diagnostic accuracy. The performance of the trained machine could be tested by new images before clinical use. Real-time diagnosis, easy to use and potential high accuracy were the advantages of AI for IOPD. In sum, AI with deep learning technology is a promising method to help rapid and accurate IOPD.Entities:
Keywords: Artificial intelligence; Deep learning; Frozen section; Intraoperative pathological diagnosis
Mesh:
Year: 2017 PMID: 28915974 DOI: 10.1016/j.mehy.2017.08.021
Source DB: PubMed Journal: Med Hypotheses ISSN: 0306-9877 Impact factor: 1.538