Karl-Friedrich Kowalewski1, Jonathan D Hendrie1, Mona W Schmidt1, Carly R Garrow1, Thomas Bruckner2, Tanja Proctor2, Sai Paul1, Davud Adigüzel3, Sebastian Bodenstedt3, Andreas Erben4, Hannes Kenngott1, Young Erben5, Stefanie Speidel3, Beat P Müller-Stich1, Felix Nickel6. 1. Department of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. 2. Department of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany. 3. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Adenauerring 2, 76131, Karlsruhe, Germany. 4. Private Person, Hanauer Landstraße 204, 60314, Frankfurt, Germany. 5. Section of Vascular and Endovascular Surgery, Yale University, New Haven, CT, USA. 6. Department of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. felix.nickel@med.uni-heidelberg.de.
Abstract
INTRODUCTION: 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. MATERIALS: 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. RESULTS: 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. CONCLUSION: 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.
INTRODUCTION: 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. MATERIALS: 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. RESULTS: 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. CONCLUSION: 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.
Entities:
Keywords:
Assessment; Computer-assisted surgery; Education; Kinect; Laparoscopic suturing and knot tying; Minimally invasive surgery
Authors: Timothy M Kowalewski; Lee W White; Thomas S Lendvay; Iris S Jiang; Robert Sweet; Andrew Wright; Blake Hannaford; Mika N Sinanan Journal: J Surg Res Date: 2014-06-04 Impact factor: 2.192
Authors: Mona W Schmidt; Karl-Friedrich Kowalewski; Sarah M Trent; Laura Benner; Beat P Müller-Stich; Felix Nickel Journal: Surg Endosc Date: 2019-05-28 Impact factor: 4.584
Authors: Bernard Gibaud; Germain Forestier; Carolin Feldmann; Giancarlo Ferrigno; Paulo Gonçalves; Tamás Haidegger; Chantal Julliard; Darko Katić; Hannes Kenngott; Lena Maier-Hein; Keno März; Elena de Momi; Dénes Ákos Nagy; Hirenkumar Nakawala; Juliane Neumann; Thomas Neumuth; Javier Rojas Balderrama; Stefanie Speidel; Martin Wagner; Pierre Jannin Journal: Int J Comput Assist Radiol Surg Date: 2018-07-13 Impact factor: 2.924
Authors: Karl-Friedrich Kowalewski; Carly R Garrow; Mona W Schmidt; Laura Benner; Beat P Müller-Stich; Felix Nickel Journal: Surg Endosc Date: 2019-02-21 Impact factor: 4.584
Authors: Sandeep Ganni; Sanne M B I Botden; Magdalena Chmarra; Richard H M Goossens; Jack J Jakimowicz Journal: Surg Endosc Date: 2018-01-16 Impact factor: 4.584
Authors: Felix von Bechtolsheim; Florian Oehme; Michael Maruschke; Sofia Schmidt; Alfred Schneider; Jürgen Weitz; Marius Distler; Sebastian Bodenstedt; Isabel Funke; Stefanie Speidel; Soeren Torge Mees Journal: Surg Endosc Date: 2021-11-15 Impact factor: 3.453
Authors: Kivanc Atesok; Peter MacDonald; Jeff Leiter; James Dubberley; Richard Satava; Ann VanHeest; Shepard Hurwitz; J Lawrence Marsh Journal: World J Orthop Date: 2017-04-18