Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon
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Training and assessment outside of the operating room is crucial for minimally invasive surgery due to steep learning curves. Thus, we have developed and validated the sensor- and expert model-based laparoscopic training system, the iSurgeon.
Participants of different experience levels (novice, intermediate, expert) performed four standardized laparoscopic knots. Instruments and surgeons’ joint motions were tracked with an NDI Polaris camera and Microsoft Kinect v1. With frame-by-frame image analysis, the key steps of suturing and knot tying were identified and registered with motion data. Construct validity, concurrent validity, and test–retest reliability were analyzed. The Objective Structured Assessment of Technical Skills (OSATS) was used as the gold standard for concurrent validity.
The system showed construct validity by discrimination between experience levels by parameters such as time (novice = 442.9 ± 238.5 s; intermediate = 190.1 ± 50.3 s; expert = 115.1 ± 29.1 s; p < 0.001), total path length (novice = 18,817 ± 10318 mm; intermediate = 9995 ± 3286 mm; expert = 7265 ± 2232 mm; p < 0.001), average speed (novice = 42.9 ± 8.3 mm/s; intermediate = 52.7 ± 11.2 mm/s; expert = 63.6 ± 12.9 mm/s; p < 0.001), angular path (novice = 20,573 ± 12,611°; intermediate = 8652 ± 2692°; expert = 5654 ± 1746°; p < 0.001), number of movements (novice = 2197 ± 1405; intermediate = 987 ± 367; expert = 743 ± 238; p < 0.001), number of movements per second (novice = 5.0 ± 1.4; intermediate = 5.2 ± 1.5; expert = 6.6 ± 1.6; p = 0.025), and joint angle range (for different axes and joints all p < 0.001). Concurrent validity of OSATS and iSurgeon parameters was established. Test–retest reliability was given for 7 out of 8 parameters. The key steps “wrapping the thread around the instrument” and “needle positioning” were most difficult to learn.
Validity and reliability of the self-developed sensor-and expert model-based laparoscopic training system “iSurgeon” were established. Using multiple parameters proved more reliable than single metric parameters. Wrapping of the needle around the thread and needle positioning were identified as difficult key steps for laparoscopic suturing and knot tying. The iSurgeon could generate automated real-time feedback based on expert models which may result in shorter learning curves for laparoscopic tasks. Our next steps will be the implementation and evaluation of full procedural training in an experimental model.
KeywordsMinimally invasive surgery Assessment Education Laparoscopic suturing and knot tying Computer-assisted surgery Kinect
The present study is part of Mr. Karl-Friedrich Kowalewski’s doctoral thesis at Heidelberg University.
The present research was conducted within the setting of the SFB/Transregio 125 “Cognition-Guided Surgery” funded by the German Research Foundation. It is also sponsored by the European Social Fund of the State Baden Wuerttemberg.
Kowalewski, Nickel, Hendrie, Müller-Stich, Speidel, and Kenngott contributed to study conception and design; Kowalewski, Schmidt, Hendrie, Garrow Paul, Bodenstedt, and Adigüzel participated in acquisition of data; Bruckner, Kowalewski, Proctor, Bodenstedt, Garrow, Kenngott, Erben A, and Adiüzel performed the statistical analysis; Kowalewski, Nickel, Bruckner, Proctor, Schmidt, Garrow, Bodenstedt, and Erben A are involved in analysis and interpretation of data; Kowalewski, Nickel, Hendrie, Paul, Garrow, and Erben Y drafted the manuscript; Müller-Stich, Speidel, Kenngott, Bruckner, and Erben Y made critical revision.
Compliance with ethical standards
Felix Nickel reports receiving travel support for conference participation as well as equipment provided for laparoscopic surgery courses by KARL STORZ, Johnson & Johnson, and Medtronic. Karl-Friedrich Kowalewski, Jonathan D Hendrie, Mona W Schmidt, Thomas Bruckner, Sai Paul, Sebastian Bodenstedt, Tanja Proctor, Carly R Garrow, Andreas Erben, Young Erben, Davud Adigüzel, Hannes G Kenngott, Stefanie Speidel, and Beat P Müller-Stich have no conflicts of interest or financial ties to disclose.
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