Mauro Zago1, Chiarella Sforza2,3, Diego Mariani4, Matteo Marconi5, Alan Biloslavo6, Antonio La Greca7, Hayato Kurihara8, Andrea Casamassima9, Samantha Bozzo1, Francesco Caputo10, Manuela Galli10, Matteo Zago11,12. 1. Department of Surgery, Minimally Invasive Surgery Unit, Policlinico San Pietro, Via Forlanini 15, Ponte San Pietro, 24036, Bergamo, Italy. 2. Department of Biomedical Sciences for Health, Università degli Studi di Milano, via Mangiagalli 31, 20133, Milan, Italy. 3. Institute of Molecular Bioimaging and Physiology, National Research Council, Segrate, Italy. 4. General Surgery Department, Legnano Hospital, ASST Ovest Milanese, Legnano, MI, Italy. 5. General Surgery Department, S. Maria delle Stelle Hospital, ASST Melegnano e Martesana, Melzo, Milan, Italy. 6. General Surgery Department, Cattinara University Hospital, Trieste, Italy. 7. Department of Surgery, Emergency Surgery Unit, Policlinico Gemelli, Catholic University of the Sacred Heart, Rome, Italy. 8. Emergency and Trauma Surgery Unit, Humanitas Research Hospital, Via Manzoni, 35, 20090, Rozzano, MI, Italy. 9. General Surgery Department, S. Maria delle Stelle Hospital, ASST Melegnano e Martesana Melzo, Cernusco sul Naviglio, MI, Italy. 10. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy. 11. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy. matteo2.zago@polimi.it. 12. Fondazione Istituto Farmacologico Filippo Serpero, Viale Luigi Majno 40, 20122, Milan, Italy. matteo2.zago@polimi.it.
Abstract
PURPOSE: Increasing pressure pushes towards the objective competence assessment of clinical operators. Hand motion analysis (HMA) was introduced to measure surgical and clinical procedures; its recent application to FAST examinations leaves unsolved issues. This study aimed at determining optimal HMA parameters to discriminate between operators' skill levels, and which FAST tasks are experience-dependent. METHODS: Ten experienced (EG) and 13 beginner (BG) sonographers performed a FAST examination on one female and one male model. A motion capture system returned the duration, working volume, number of movements (absolute and time normalized), and hand path length (absolute and time normalized) of each view. RESULTS: BG took more time in completing specific views, with a higher working volume (p = 0.003) and longer hands path (p < 0.001). The number of movements was lower in the EG (p < 0.001) and differed between views (p = 0.014). No significant Group/Model differences were found for the normalized number of movements. The LUQ view required a higher number of movements (p < 0.001). CONCLUSIONS: HMA identified kinematic parameters discriminating between proficiency level and critical subtasks in the FAST examination. These findings could be the base for a focused HMA-based evaluation of performances following a proctored training period. There is room to incorporate HMA into simulation metrics and evidence-based credentialing standards for clinical ultrasound applications.
PURPOSE: Increasing pressure pushes towards the objective competence assessment of clinical operators. Hand motion analysis (HMA) was introduced to measure surgical and clinical procedures; its recent application to FAST examinations leaves unsolved issues. This study aimed at determining optimal HMA parameters to discriminate between operators' skill levels, and which FAST tasks are experience-dependent. METHODS: Ten experienced (EG) and 13 beginner (BG) sonographers performed a FAST examination on one female and one male model. A motion capture system returned the duration, working volume, number of movements (absolute and time normalized), and hand path length (absolute and time normalized) of each view. RESULTS: BG took more time in completing specific views, with a higher working volume (p = 0.003) and longer hands path (p < 0.001). The number of movements was lower in the EG (p < 0.001) and differed between views (p = 0.014). No significant Group/Model differences were found for the normalized number of movements. The LUQ view required a higher number of movements (p < 0.001). CONCLUSIONS:HMA identified kinematic parameters discriminating between proficiency level and critical subtasks in the FAST examination. These findings could be the base for a focused HMA-based evaluation of performances following a proctored training period. There is room to incorporate HMA into simulation metrics and evidence-based credentialing standards for clinical ultrasound applications.
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