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Med Hypotheses. 2017 Sep;107:98-99. doi: 10.1016/j.mehy.2017.08.021. Epub 2017 Sep 1.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

Author information

1
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, PR China; Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, PR China.
2
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, PR China.
3
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, PR China.
4
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, PR China. Electronic address: drmaxuelei@gmail.com.

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.

KEYWORDS:

Artificial intelligence; Deep learning; Frozen section; Intraoperative pathological diagnosis

PMID:
28915974
DOI:
10.1016/j.mehy.2017.08.021
[Indexed for MEDLINE]

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