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Towards Automatic Skill Evaluation in Microsurgery

Authors:

Enkelejda KasneciAuthors Info & Claims

IUI '17 Companion: Companion Proceedings of the 22nd International Conference on Intelligent User Interfaces

Pages 73 - 76

https://doi.org/10.1145/3030024.3040985

Published: 07 March 2017 Publication History

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Abstract

In the past decade, eye tracking has emerged as a promising answer to the increasing needs of understanding surgical expertise. The implicit desire is to design an intelligent user interface (IUI) to monitor and assess the competency of surgical trainees. In this paper, for the first time in microsurgery, we explore the potential for a surgical automatic skill assessment through a combination of machine learning techniques, computational modeling, and eye tracking. We present primary findings from a random forest classification method where we achieved about 70% recognition rate for the detection of expert and novice group. This leads us to a conclusion that prediction of the micro-surgeon performance is possible, can be automated, and that the eye movement data carry important information about the skills of micro-surgeons.

References

[1]

N. Ahmidi, G. D. Hager, L. Ishii, G. Fichtinger, G. L. Gallia, and M. Ishii. 2010. Surgical task and skill classi?cation from eye tracking and tool motion in minimally invasive surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 295?302.

Digital Library

Google Scholar

[2]

R. Bertram, L. Helle, J.K. Kaakinen, and E. Svedström. 2013. The effect of expertise on eye movement behaviour in medical image perception. PloS one 8, 6 (2013), e66169.

Crossref

Google Scholar

[3]

S. Eivazi. 2016. Eye gaze patterns in micro-neurosurgery: from remote to ocular-based eye tracker. Ph.D. Dissertation. University of Eastern Finland, Dissertations in Forestry and Natural Sciences.

Google Scholar

[4]

S. Eivazi, R. Bednarik, M. Tukiainen, M. von und zu Fraunberg, V. Leinonen, and J. E. Jääskeläinen. 2012. Gaze behaviour of expert and novice microneurosurgeons differs during observations of tumor removal recordings. In Proceedings of the Symposium on Eye Tracking Research and Applications. ACM, 377?380.

Digital Library

Google Scholar

[5]

F. Hermens, R. Flin, and I. Ahmed. 2013. Eye movements in surgery: A literature review. Journal of Eye Movement Research 6, 4 (2013).

Crossref

Google Scholar

[6]

T. Kübler, S. Eivazi, and E. Kasneci. 2015. Automated Visual Scanpath Analysis Reveals the Expertise Level of Micro-neurosurgeons. In MICCAI Workshop on Interventional Microscopy.

Google Scholar

[7]

T. C. Kübler, C. Rothe, U. Schiefer, W. Rosenstiel, and E. Kasneci. 2016. SubsMatch 2.0: Scanpath comparison and classi?cation based on subsequence frequencies. Behavior Research Methods (2016), 1?17.

Google Scholar

[8]

H. L. Kundel and P. S. La Follette Jr. 1972. Visual search patterns and experience with radiological images 1. Radiology 103, 3 (1972), 523?528.

Google Scholar

[9]

S. Ramachandran, A. M. Ghanem, and S. R. Myers. 2013. Assessment of microsurgery competency-where are we now? Microsurgery 33, 5 (2013), 406?415.

Google Scholar

[10]

L. Richstone, M. J. Schwartz, C. Seideman, J. Cadeddu, S. Marshall, and L. R. Kavoussi. 2010. Eye metrics as an objective assessment of surgical skill. Annals of surgery 252, 1 (2010), 177?182.

Crossref

Google Scholar

[11]

Dario D Salvucci and Joseph H Goldberg. 2000. Identifying ?xations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications. ACM, 71?78.

Digital Library

Google Scholar

[12]

T. Tien, P. H. Pucher, M. H. Sodergren, K. Sriskandarajah, G. Yang, and A. Darzi. 2014. Eye tracking for skills assessment and training: a systematic review. journal of surgical research 191, 1 (2014), 169?178.

Google Scholar

Cited By

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Kędras MSobecki J(2023)What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye TrackingInformation10.3390/info1411062414:11(624)Online publication date: 20-Nov-2023
https://doi.org/10.3390/info14110624
Toy SOzsoy SShafiei SAntonenko PSchwengel D(2023)Using electroencephalography to explore neurocognitive correlates of procedural proficiency: A pilot study to compare experts and novices during simulated endotracheal intubationBrain and Cognition10.1016/j.bandc.2022.105938165(105938)Online publication date: Feb-2023
https://doi.org/10.1016/j.bandc.2022.105938
Wang FKreiner TLutz ALohmeyer QMeboldt M(2023)What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural taskUser Modeling and User-Adapted Interaction10.1007/s11257-022-09352-933:4(939-965)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1007/s11257-022-09352-9
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Index Terms

Towards Automatic Skill Evaluation in Microsurgery
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information

Published In

IUI '17 Companion: Companion Proceedings of the 22nd International Conference on Intelligent User Interfaces

March 2017

246 pages

ISBN:9781450348935

DOI:10.1145/3030024

General Chairs:
George A. Papadopoulos
The University of Cyprus, Cyprus
,
Tsvi Kuflik
The University of Haifa, Israel
,
Program Chairs:
Fang Chen
Data61, CSIRO Australia
,
Carlos Duarte
The University of Lisbon, Portugal
,
Wai-Tat Fu
The University of Illinois at Urbana-Champaign, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2017

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IUI'17

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IUI'17: 22nd International Conference on Intelligent User Interfaces

March 13 - 16, 2017

Limassol, Cyprus

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IUI '17 Companion Paper Acceptance Rate 63 of 272 submissions, 23%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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30th International Conference on Intelligent User Interfaces

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Cited By

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Kędras MSobecki J(2023)What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye TrackingInformation10.3390/info1411062414:11(624)Online publication date: 20-Nov-2023
https://doi.org/10.3390/info14110624
Toy SOzsoy SShafiei SAntonenko PSchwengel D(2023)Using electroencephalography to explore neurocognitive correlates of procedural proficiency: A pilot study to compare experts and novices during simulated endotracheal intubationBrain and Cognition10.1016/j.bandc.2022.105938165(105938)Online publication date: Feb-2023
https://doi.org/10.1016/j.bandc.2022.105938
Wang FKreiner TLutz ALohmeyer QMeboldt M(2023)What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural taskUser Modeling and User-Adapted Interaction10.1007/s11257-022-09352-933:4(939-965)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1007/s11257-022-09352-9
Hosp BSchultz FKasneci EHöner O(2021)Expertise Classification of Soccer Goalkeepers in Highly Dynamic Decision Tasks: A Deep Learning Approach for Temporal and Spatial Feature Recognition of Fixation Image Patch SequencesFrontiers in Sports and Active Living10.3389/fspor.2021.6925263Online publication date: 26-Jul-2021
https://doi.org/10.3389/fspor.2021.692526
Hosp BSchultz FHöner OKasneci E(2021)Soccer goalkeeper expertise identification based on eye movementsPLOS ONE10.1371/journal.pone.025107016:5(e0251070)Online publication date: 19-May-2021
https://doi.org/10.1371/journal.pone.0251070
Fuhl WKasneci GKasneci E(2021)TEyeD: Over 20 Million Real-World Eye Images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR52148.2021.00053(367-375)Online publication date: Oct-2021
https://doi.org/10.1109/ISMAR52148.2021.00053
Fuhl WRong YKasneci E(2021)Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9413268(142-149)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9413268
Fuhl WRong YMotz TScheidt MHartel AKoch AKasneci E(2021)Explainable Online Validation of Machine Learning Models for Practical Applications2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412959(3304-3311)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412959
Fuhl WSchneider JKasneci E(2021)1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW54120.2021.00386(3459-3469)Online publication date: Oct-2021
https://doi.org/10.1109/ICCVW54120.2021.00386
Yin MHaddawy PHosp BSa-ngasoongsong PTanprathumwong TSayo MYangyuenpradorn SSupratak A(2020)A Study of Expert/Novice Perception in Arthroscopic Shoulder SurgeryProceedings of the 4th International Conference on Medical and Health Informatics10.1145/3418094.3418135(71-77)Online publication date: 14-Aug-2020
https://dl.acm.org/doi/10.1145/3418094.3418135

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