Dataset Information

Intraoperative head drift and eye movement: two under addressed challenges during cataract surgery.

ABSTRACT: PURPOSE:To objectively measure head drift during cataract surgery, and subjectively simulate eye movements and assess impact on surgical technique. MATERIALS AND METHODS:Twelve consecutively recorded routine cataract operations in the Tennent Institute of Ophthalmology, Gartnavel General Hospital, Glasgow, were reviewed. The speculum was used as a fixed point and correlated with a superimposed virtual ruler to measure maximum head drift in each direction throughout the operations. To simulate intraoperative eye movement, we attached string to the cataract surgical simulator (Eyesi) eye and manually induced abduction and adduction. A calibrated scale secured to the Eyesi head ensured 5?mm eye movements were consistently created. Ophthalmology trainees performed the continuous curvilinear capsulorhexis (CCC) exercise without and with sequential eye movements. Movements were induced every three seconds. Scores were compared using a paired Student's T-test. RESULTS:Mean head drift in the surgical recordings was 3.1?mm medially (range 2-7?mm), 2.9?mm laterally (range 2-4?mm), 2.6?mm superiorly (range 1-5?mm), and 1.9?mm inferiorly (range 1-4?mm). In 11 of 12 cases, the operating microscope had to be adjusted for head drift. Six junior trainees completed the CCC module on the Eyesi without then with eye movements. After introducing eye movements the mean Eyesi score reduced from 92.7 to 76.9 (P?=?0.014), 'roundness of rhexis' score reduced from 89.4 to 57.5 (P?=?0.020), and trainees operated 17?s faster (P?=?0.016). CONCLUSION:This study objectively demonstrates the under-reported clinical scenario of head drift during cataract surgery. By manipulating the Eyesi we have shown that eye movements reduce the quality of cataract surgery.

SUBMITTER: Brogan K

PROVIDER: S-EPMC5997653 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Intraoperative head drift and eye movement: two under addressed challenges during cataract surgery.

Brogan Kerr K Dawar Basu B Lockington David D Ramaesh Kanna K

Eye (London, England) 20180221 6

<h4>Purpose</h4>To objectively measure head drift during cataract surgery, and subjectively simulate eye movements and assess impact on surgical technique.<h4>Materials and methods</h4>Twelve consecutively recorded routine cataract operations in the Tennent Institute of Ophthalmology, Gartnavel General Hospital, Glasgow, were reviewed. The speculum was used as a fixed point and correlated with a superimposed virtual ruler to measure maximum head drift in each direction throughout the operations. ...[more]

PMID: 29463862

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Project description:ImportanceComplications that arise from phacoemulsification procedures can lead to worse visual outcomes. Real-time image processing with artificial intelligence tools can extract data to deliver surgical guidance, potentially enhancing the surgical environment.ObjectiveTo evaluate the ability of a deep neural network to track the pupil, identify the surgical phase, and activate specific computer vision tools to aid the surgeon during phacoemulsification cataract surgery by providing visual feedback in real time.Design, setting, and participantsThis cross-sectional study evaluated deidentified surgical videos of phacoemulsification cataract operations performed by faculty and trainee surgeons in a university-based ophthalmology department between July 1, 2020, and January 1, 2021, in a population-based cohort of patients.ExposuresA region-based convolutional neural network was used to receive frames from the video source and, in real time, locate the pupil and in parallel identify the surgical phase being performed. Computer vision-based algorithms were applied according to the phase identified, providing visual feedback to the surgeon.Main outcomes and measuresOutcomes were area under the receiver operator characteristic curve and area under the precision-recall curve for surgical phase classification and Dice score (harmonic mean of the precision and recall [sensitivity]) for detection of the pupil boundary. Network performance was assessed as video output in frames per second. A usability survey was administered to volunteer cataract surgeons previously unfamiliar with the platform.ResultsThe region-based convolutional neural network model achieved area under the receiver operating characteristic curve values of 0.996 for capsulorhexis, 0.972 for phacoemulsification, 0.997 for cortex removal, and 0.880 for idle phase recognition. The final algorithm reached a Dice score of 90.23% for pupil segmentation and a mean (SD) processing speed of 97 (34) frames per second. Among the 11 cataract surgeons surveyed, 8 (72%) were mostly or extremely likely to use the current platform during surgery for complex cataract.Conclusions and relevanceA computer vision approach using deep neural networks was able to pupil track, identify the surgical phase being executed, and activate surgical guidance tools. These results suggest that an artificial intelligence-based surgical guidance platform has the potential to enhance the surgeon experience in phacoemulsification cataract surgery. This proof-of-concept investigation suggests that a pipeline from a surgical microscope could be integrated with neural networks and computer vision tools to provide surgical guidance in real time.

Dataset Information

Intraoperative head drift and eye movement: two under addressed challenges during cataract surgery.

Publications

Intraoperative head drift and eye movement: two under addressed challenges during cataract surgery.

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