Artificial intelligence technology based on deep learning in digestive endoscopy imaging diagnosis

Abstract

With the continuous progress in the era of big data, artificial intelligence technology has begun to get more and more applications in medicine, and it is becoming more and more possible to use artificial intelligence in the diagnostic technology of digestive endoscopy images. This article mainly studies the progress of artificial intelligence technology in the diagnosis of gastrointestinal endoscopy. The main purpose is to add new vitality to the medical conditions that are not yet perfect. This article mainly uses digestive endoscopy to carry out a comprehensive examination of all aspects of the digestive tract under the conditions of accuracy, clarity, and other advantages. Randomly select 14 of the 50 registered patient images for corresponding gastrointestinal endoscopy. The premise is that the subjects must be tested under the same conditions. The ordinary manual detection technology adds 4 groups as the control group, and the start time of the test is the same; when conducting the experiment, the position of each group to be tested must be the same and the detection area is large enough. Finally, image simulation is carried out on the experiment. The experimental results show that the sensitivity of artificial intelligence used in the actual experiment is 95.1%, of which the most prominent data value is 97.6%. The accuracy of the digestive endoscopy is 96.6%. The sharpness of the image detected by artificial intelligence is also superior.

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Funding

This study was supported by Grant No. 2019JZZY011101 from the Key Research and Development Program of Shandong Province to Dianmin Sun.

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Correspondence to Dianmin Sun.

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Cheng, J., Song, T., Liu, Z. et al. Artificial intelligence technology based on deep learning in digestive endoscopy imaging diagnosis. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01532-5

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Keywords

  • Artificial intelligence technology
  • Digestion of endoscopic images
  • Clear detection
  • Image simulation
  • Endoscopic detection technology
  • Medical imaging