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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 1/2020

11.10.2019 | Original Article

Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches

verfasst von: Fernando Pérez-Escamirosa, Antonio Alarcón-Paredes, Gustavo Adolfo Alonso-Silverio, Ignacio Oropesa, Oscar Camacho-Nieto, Daniel Lorias-Espinoza, Arturo Minor-Martínez

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 1/2020

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Abstract

Background

The determination of surgeons’ psychomotor skills in minimally invasive surgery techniques is one of the major concerns of the programs of surgical training in several hospitals. Therefore, it is important to assess and classify objectively the level of experience of surgeons and residents during their training process. The aim of this study was to investigate three classification methods for establishing automatically the level of surgical competence of the surgeons based on their psychomotor laparoscopic skills.

Methods

A total of 43 participants, divided into an experienced surgeons group with ten experts (> 100 laparoscopic procedures performed) and non-experienced surgeons group with 24 residents and nine medical students (< 10 laparoscopic procedures performed), performed three tasks in the EndoViS training system. Motion data of the instruments were captured with a video-tracking system built into the EndoViS simulator and analyzed using 13 motion analysis parameters (MAPs). Radial basis function networks (RBFNets), K-star (K*), and random forest (RF) were used for classifying surgeons based on the MAPs’ scores of all participants. The performance of the three classifiers was examined using hold-out and leave-one-out validation techniques.

Results

For all three tasks, the K-star method was superior in terms of accuracy and AUC in both validation techniques. The mean accuracy of the classifiers was 93.33% for K-star, 87.58% for RBFNets, and 84.85% for RF in hold-out validation, and 91.47% for K-star, 89.92% for RBFNets, and 83.72% for RF in leave-one-out cross-validation.

Conclusions

The three proposed methods demonstrated high performance in the classification of laparoscopic surgeons, according to their level of psychomotor skills. Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscopic box trainers.
Literatur
1.
Zurück zum Zitat Cuschieri A (2005) Laparoscopic surgery: current status, issues and future developments. Surgeon 3:125–130CrossRef Cuschieri A (2005) Laparoscopic surgery: current status, issues and future developments. Surgeon 3:125–130CrossRef
9.
Zurück zum Zitat Bansal VK, Raveendran R, Misra MC, Bhattacharjee H, Rajan K, Krishna A, Kumar P, Kumar S (2014) A prospective randomized controlled blinded study to evaluate the effect of short-term focused training program in laparoscopy on operating room performance of surgery residents (CTRI /2012/11/003113). J Surg Educ 71:52–60. https://doi.org/10.1016/j.jsurg.2013.06.012 CrossRefPubMed Bansal VK, Raveendran R, Misra MC, Bhattacharjee H, Rajan K, Krishna A, Kumar P, Kumar S (2014) A prospective randomized controlled blinded study to evaluate the effect of short-term focused training program in laparoscopy on operating room performance of surgery residents (CTRI /2012/11/003113). J Surg Educ 71:52–60. https://​doi.​org/​10.​1016/​j.​jsurg.​2013.​06.​012 CrossRefPubMed
13.
Zurück zum Zitat Pérez-Escamirosa F, Chousleb-Kalach A, Hernández-Baro MD, Sánchez-Margallo JA, Lorias-Espinoza D, Minor-Martínez A (2016) Construct validity of a video-tracking system based on orthogonal cameras approach for objective assessment of laparoscopic skills. Int J Comput Assist Radiol Surg 11:2283–2293. https://doi.org/10.1007/s11548-016-1388-1 CrossRefPubMed Pérez-Escamirosa F, Chousleb-Kalach A, Hernández-Baro MD, Sánchez-Margallo JA, Lorias-Espinoza D, Minor-Martínez A (2016) Construct validity of a video-tracking system based on orthogonal cameras approach for objective assessment of laparoscopic skills. Int J Comput Assist Radiol Surg 11:2283–2293. https://​doi.​org/​10.​1007/​s11548-016-1388-1 CrossRefPubMed
14.
Zurück zum Zitat Oropesa I, Sánchez-González P, Chmarra MK, Lamata P, Fernández A, Sánchez-Margallo JA, Jansen FW, Dankelman J, Sánchez-Margallo FM, Gómez EJ (2013) EVA: laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment. Surg Endosc 27:1029–1039. https://doi.org/10.1007/s00464-012-2513-z CrossRefPubMed Oropesa I, Sánchez-González P, Chmarra MK, Lamata P, Fernández A, Sánchez-Margallo JA, Jansen FW, Dankelman J, Sánchez-Margallo FM, Gómez EJ (2013) EVA: laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment. Surg Endosc 27:1029–1039. https://​doi.​org/​10.​1007/​s00464-012-2513-z CrossRefPubMed
17.
21.
Zurück zum Zitat Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: MICCAI workshop: M2CAI, vol 3, p 3 Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: MICCAI workshop: M2CAI, vol 3, p 3
22.
Zurück zum Zitat Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. Int J Comput Assist Radiol Surg 13(5):731–739CrossRef Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. Int J Comput Assist Radiol Surg 13(5):731–739CrossRef
23.
Zurück zum Zitat Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. In: MICCAI, pp 214–221 Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. In: MICCAI, pp 214–221
24.
Zurück zum Zitat Wang Z, Majewicz Fey A (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 13(12):1959–1970CrossRef Wang Z, Majewicz Fey A (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 13(12):1959–1970CrossRef
33.
Zurück zum Zitat Pérez F, Sossa H, Martínez R, Lorias D, Minor A (2013) Video-based tracking of laparoscopic instruments using an orthogonal webcams system. World Acad Sci Eng Technol Int J Medical Heal Biomed Bioeng Pharm Eng 7:440–443 Pérez F, Sossa H, Martínez R, Lorias D, Minor A (2013) Video-based tracking of laparoscopic instruments using an orthogonal webcams system. World Acad Sci Eng Technol Int J Medical Heal Biomed Bioeng Pharm Eng 7:440–443
37.
Zurück zum Zitat Cleary JG, Cleary JG, Trigg LE (1995) K*: an instance-based learner using an entropic distance measure. in: Proceedings of 12TH international conference on machine learning, pp 108–114CrossRef Cleary JG, Cleary JG, Trigg LE (1995) K*: an instance-based learner using an entropic distance measure. in: Proceedings of 12TH international conference on machine learning, pp 108–114CrossRef
38.
Zurück zum Zitat Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest?. Springer, Berlin, pp 154–168 Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest?. Springer, Berlin, pp 154–168
Metadaten
Titel
Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches
verfasst von
Fernando Pérez-Escamirosa
Antonio Alarcón-Paredes
Gustavo Adolfo Alonso-Silverio
Ignacio Oropesa
Oscar Camacho-Nieto
Daniel Lorias-Espinoza
Arturo Minor-Martínez
Publikationsdatum
11.10.2019
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 1/2020
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-02073-2

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