Surgical Medical Education Via 3D Bioprinting: Modular System for Endovascular Training
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There is currently a shift in surgical training from traditional methods to simulation-based approaches, recognizing the necessity of more effective and controlled learning environments. This study introduces a completely new 3D-printed modular system for endovascular surgery training (M-SET), developed to allow various difficulty levels. Its design was based on computed tomography angiographies from real patient data with femoro-popliteal lesions. The study aimed to explore the integration of simulation training via a 3D model into the surgical training curriculum and its effect on their performance. Our preliminary study included 12 volunteer trainees randomized 1:1 into the standard simulation (SS) group (3 stepwise difficulty training sessions) and the random simulation (RS) group (random difficulty of the M-SET). A senior surgeon evaluated and timed the final training session. Feedback reports were assessed through the Student Satisfaction and Self-Confidence in Learning Scale. The SS group completed the training sessions in about half time (23.13 ± 9.2 min vs. 44.6 ± 12.8 min). Trainees expressed high satisfaction with the training program supported by the M-SET. Our 3D-printed modular training model meets the current need for new endovascular training approaches, offering a customizable, accessible, and effective simulation-based educational program with the aim of reducing the time required to reach a high level of practical skills.
Mersanne A, Foresti R, Martini C, Caffarra Malvezzi C, Rossi G, Fornasari A Diagnostics (Basel). 2025; 15(1.
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Dey R, Guo Y, Liu Y, Puri A, Savastano L, Zheng Y Int J Comput Assist Radiol Surg. 2024; 20(2):333-344.
PMID: 39370493 DOI: 10.1007/s11548-024-03279-9.
Cin M, Koka K, Darragh J, Nourmohammadi Z, Hamdan U, Zopf D Biomimetics (Basel). 2024; 9(8).
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Catasta A, Martini C, Mersanne A, Foresti R, Bianchini Massoni C, Freyrie A Diagnostics (Basel). 2024; 14(15).
PMID: 39125534 PMC: 11312310. DOI: 10.3390/diagnostics14151658.