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Machine learning in the optimization of robotics in the operative field

Ma, Runzhuo ; Vanstrum, Erik B ; Lee, Ryan ; Chen, Jian ; Hung, Andrew J

Current opinion in urology, 2020-11, Vol.30 (6), p.808-816

United States: Copyright Wolters Kluwer Health, Inc. All rights reserved

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  • Título:
    Machine learning in the optimization of robotics in the operative field
  • Autor: Ma, Runzhuo ; Vanstrum, Erik B ; Lee, Ryan ; Chen, Jian ; Hung, Andrew J
  • Assuntos: Algorithms ; Clinical Competence ; Female ; Female Urogenital Diseases - surgery ; Humans ; Machine Learning ; Male ; Male Urogenital Diseases - surgery ; Patient Selection ; Robotic Surgical Procedures - methods ; Robotic Surgical Procedures - standards ; Robotics ; Urologic Surgical Procedures - methods ; Urologic Surgical Procedures - standards
  • É parte de: Current opinion in urology, 2020-11, Vol.30 (6), p.808-816
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-3
    content type line 23
    ObjectType-Review-2
  • Descrição: PURPOSE OF REVIEWThe increasing use of robotics in urologic surgery facilitates collection of ‘big data’. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT FINDINGSMachine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARYRobot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.
  • Editor: United States: Copyright Wolters Kluwer Health, Inc. All rights reserved
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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