August 2021
Volume 62, Issue 11
Open Access
ARVO Imaging in the Eye Conference Abstract  |   August 2021
An Automated Algorithm for Tool Tracking and Turbulence Detection During Phacoemulsification Cataract Surgery Using Computer Vision and Deep Learning Neural Networks
Author Affiliations & Notes
  • Rogerio Garcia Nespolo
    Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
    Ophthalmology & Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Yannek Isaac Leiderman
    Ophthalmology & Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
    Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Cristian Luciano
    Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Nita Valikodath
    Ophthalmology & Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Emily Cole
    Ophthalmology & Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Footnotes
    Commercial Relationships   Rogerio Garcia Nespolo, None; Yannek Leiderman, None; Cristian Luciano, None; Nita Valikodath, None; Emily Cole, None
  • Footnotes
    Support  Work supported by the University of Illinois at Chicago Louis and Dolores Jedd Research Awardee
Investigative Ophthalmology & Visual Science August 2021, Vol.62, 30. doi:
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      Rogerio Garcia Nespolo, Yannek Isaac Leiderman, Cristian Luciano, Nita Valikodath, Emily Cole; An Automated Algorithm for Tool Tracking and Turbulence Detection During Phacoemulsification Cataract Surgery Using Computer Vision and Deep Learning Neural Networks. Invest. Ophthalmol. Vis. Sci. 2021;62(11):30.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Turbulent intraocular fluid flow and brusque or rapid tool movement during phacoemulsification cataract surgery can lead to potential complications such as the rupture of the lens capsular bag. Quantifying these variables and providing feedback to the surgeon in real time, or the automated control of fluidic parameters using these data, may help to enhance the surgical environment.

Methods : Segmentation of the pupil area was performed in real time by a neural network, then optical flow was used to track features inside the capsular bag. Under high-flow conditions during aspiration of lens fragments, the algorithm was able to calculate the movement of lens material and motion of tools located within the pupil area. The sensitivity threshold for motion estimation during the procedure may be varied according to the user preference. We evaluated the performance of our algorithm by comparing computed values with manual annotation of surgical videos by ophthalmic surgeons.

Results : The experts’ annotation comparing the real size of the pupil with the pupil area detected by the algorithm resulted in an overall precision, recall and interception over union (IoU) of 82.07%, 87.19% and 95.14%, respectively.
Post-processed video streaming was achieved at rates greater than 60 frames per second, demonstrating feasibility for implementation in real-time. All ophthalmic surgeons invited evaluated the tools as useful, while 86% of the participants considered the features presented intuitive.

Conclusions : Feedback to the surgeon relating to turbulent flow of lens fragments and/or brusque movements of microsurgical tools may potentially enhance the surgeon’s experience during cataract surgery. Additional studies are required to assess the feasibility for implementation into our current surgical paradigm.

This is a 2021 Imaging in the Eye Conference abstract.

 

Figure 1 - Top left: when no movement is identified, no visual feedback is displayed except the proposed rhexis size as a safe zone area; Bottom left: tracking of lens fragments during aspiration of tissues: when high flow or brusque movement is detected, the outer circle thickness is altered to provide the feedback; Right: Motion estimation data over time, considering the acceleration of elements (first order derivative). The red-dotted lines demonstrate the threshold for the alarm, which the surgeon can adjust according to the user preferences.

Figure 1 - Top left: when no movement is identified, no visual feedback is displayed except the proposed rhexis size as a safe zone area; Bottom left: tracking of lens fragments during aspiration of tissues: when high flow or brusque movement is detected, the outer circle thickness is altered to provide the feedback; Right: Motion estimation data over time, considering the acceleration of elements (first order derivative). The red-dotted lines demonstrate the threshold for the alarm, which the surgeon can adjust according to the user preferences.

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