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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

29.01.2018 | Original Article

Video and accelerometer-based motion analysis for automated surgical skills assessment

verfasst von: Aneeq Zia, Yachna Sharma, Vinay Bettadapura, Eric L. Sarin, Irfan Essa

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2018

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Abstract

Purpose

Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data).

Methods

We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce “entropy-based” features—approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment.

Results

We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment.

Conclusion

Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.

Vorheriger Artikel Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

Nächster Artikel Nonlinear deformation of tractography in ultrasound-guided low-grade gliomas resection

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Titel: Video and accelerometer-based motion analysis for automated surgical skills assessment
verfasst von: Aneeq Zia
Yachna Sharma
Vinay Bettadapura
Eric L. Sarin
Irfan Essa
Publikationsdatum: 29.01.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1704-z

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Abstract

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