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Evolution of a surgical system using deep learning in minimally invasive surgery (Review)

  • Authors:
    • Kenbun Sone
    • Saki Tanimoto
    • Yusuke Toyohara
    • Ayumi Taguchi
    • Yuichiro Miyamoto
    • Mayuyo Mori
    • Takayuki Iriyama
    • Osamu Wada-Hiraike
    • Yutaka Osuga
  • View Affiliations / Copyright

    Affiliations: Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
    Copyright: © Sone et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 45
    |
    Published online on: May 30, 2023
       https://doi.org/10.3892/br.2023.1628
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Abstract

Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing. In the present review, studies that focused on the following topics were selected: i) Organ and anatomy identification, ii) instrument identification, iii) procedure and surgical phase recognition, iv) surgery‑time prediction, v) identification of an appropriate incision line, and vi) surgical education. The development of autonomous surgical robots is also progressing, with the Smart Tissue Autonomous Robot (STAR) and RAVEN systems being the most reported developments. STAR, in particular, is currently being used in laparoscopic imaging to recognize the surgical site from laparoscopic images and is in the process of establishing an automated suturing system, albeit in animal experiments. The present review examined the possibility of fully autonomous surgical robots in the future.
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Copy and paste a formatted citation
Spandidos Publications style
Sone K, Tanimoto S, Toyohara Y, Taguchi A, Miyamoto Y, Mori M, Iriyama T, Wada-Hiraike O and Osuga Y: Evolution of a surgical system using deep learning in minimally invasive surgery (Review). Biomed Rep 19: 45, 2023.
APA
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M. ... Osuga, Y. (2023). Evolution of a surgical system using deep learning in minimally invasive surgery (Review). Biomedical Reports, 19, 45. https://doi.org/10.3892/br.2023.1628
MLA
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M., Iriyama, T., Wada-Hiraike, O., Osuga, Y."Evolution of a surgical system using deep learning in minimally invasive surgery (Review)". Biomedical Reports 19.1 (2023): 45.
Chicago
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M., Iriyama, T., Wada-Hiraike, O., Osuga, Y."Evolution of a surgical system using deep learning in minimally invasive surgery (Review)". Biomedical Reports 19, no. 1 (2023): 45. https://doi.org/10.3892/br.2023.1628
Copy and paste a formatted citation
x
Spandidos Publications style
Sone K, Tanimoto S, Toyohara Y, Taguchi A, Miyamoto Y, Mori M, Iriyama T, Wada-Hiraike O and Osuga Y: Evolution of a surgical system using deep learning in minimally invasive surgery (Review). Biomed Rep 19: 45, 2023.
APA
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M. ... Osuga, Y. (2023). Evolution of a surgical system using deep learning in minimally invasive surgery (Review). Biomedical Reports, 19, 45. https://doi.org/10.3892/br.2023.1628
MLA
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M., Iriyama, T., Wada-Hiraike, O., Osuga, Y."Evolution of a surgical system using deep learning in minimally invasive surgery (Review)". Biomedical Reports 19.1 (2023): 45.
Chicago
Sone, K., Tanimoto, S., Toyohara, Y., Taguchi, A., Miyamoto, Y., Mori, M., Iriyama, T., Wada-Hiraike, O., Osuga, Y."Evolution of a surgical system using deep learning in minimally invasive surgery (Review)". Biomedical Reports 19, no. 1 (2023): 45. https://doi.org/10.3892/br.2023.1628
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