Fernando Pérez-Escamirosa1,2, Antonio Alarcón-Paredes3, Gustavo Adolfo Alonso-Silverio4, Ignacio Oropesa5, Oscar Camacho-Nieto6, Daniel Lorias-Espinoza7, Arturo Minor-Martínez7. 1. Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Circuito Exterior S/N, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico. 2. Department of Biomedical Informatics, Faculty of Medicine, Universidad Nacional Autónoma de México (UNAM), Circuito Interior, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico. 3. Laboratory of Computing Technologies and Electronics, Faculty of Engineering, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, 39070, Chilpancingo, Guerrero, Mexico. aalarcon@uagro.mx. 4. Laboratory of Computing Technologies and Electronics, Faculty of Engineering, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, 39070, Chilpancingo, Guerrero, Mexico. 5. Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain. 6. Intelligent Computing Laboratory, Centro de Innovación y Desarrollo Tecnológico en Computación (CIDETEC-IPN), Av. Juan de Dios Bátiz, Col. Nueva Industrial Vallejo, 07700, Ciudad de México, Mexico. 7. Department of Electrical Engineering, Bioelectronics Section, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Ciudad de México, Mexico.
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.
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.
Authors: Magdalena K Chmarra; Stefan Klein; Joost C F de Winter; Frank-Willem Jansen; Jenny Dankelman Journal: Surg Endosc Date: 2009-11-14 Impact factor: 4.584
Authors: Reinhard Fuchs; Karel M Van Praet; Richard Bieck; Jörg Kempfert; David Holzhey; Markus Kofler; Michael A Borger; Stephan Jacobs; Volkmar Falk; Thomas Neumuth Journal: Int J Comput Assist Radiol Surg Date: 2022-03-16 Impact factor: 3.421