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Related Concept Videos

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Artificial intelligence, robotics and eye surgery: are we overfitted?

Müller G Urias1,2, Niravkumar Patel3, Changyan He3,4

  • 1Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD 21287 USA.

International Journal of Retina and Vitreous
|January 1, 2020
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Summary

Robotic systems augmented by artificial intelligence may improve robot-assisted retinal surgery. Machine learning

Keywords:
Artificial intelligenceOphthalmologyRetinaRobotic surgical proceduresRobotics

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Area of Science:

  • Ophthalmology
  • Robotics
  • Artificial Intelligence

Background:

  • Retinal micro-surgery demands extreme precision, pushing human limits.
  • Robotic assistance in ophthalmic surgery faces adoption challenges despite development efforts.
  • The integration of artificial intelligence (AI) and machine learning (ML) in ophthalmology is accelerating.

Purpose of the Study:

  • To analyze advances in robotic retinal surgery.
  • To identify current drawbacks and limitations of robotic-assisted retinal surgery.
  • To explore the potential role of AI in enhancing robotic retinal surgery.

Main Methods:

  • Review of current literature on robotic retinal surgery.
  • Analysis of technological advancements, including AI and machine learning.
  • Discussion of challenges and future potential.

Main Results:

  • Significant delays exist between the conception and application of robotic and AI technologies in retinal surgery.
  • Advancements in graphics processing units have enabled progress in machine learning for surgical applications.
  • Current AI/ML capabilities in surgery, while promising, may exceed their current developmental stage.

Conclusions:

  • Robotic systems, especially when augmented by machine learning, hold the potential to significantly improve robot-assisted retinal surgery.
  • AI and ML could transform the field of ophthalmic surgery by enabling more complex procedures.
  • Further technological development is necessary for the full realization of AI's potential in robotic retinal surgery.