Systematic Review on the Use of 3D-Printed Models for Planning, Training and Simulation in Vascular Surgery.

The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women's Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians' confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents.

Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs' Parenchymal Involvement Quantification in COVID-19 Patients.

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland-Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1-R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1-R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.

Surgical Medical Education via 3D Bioprinting: Modular System for Endovascular Training.

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.