Dataset Information

Embolization of visceral arterial aneurysms: Simulation with 3D-printed models.

ABSTRACT: OBJECTIVES:The present technical article aimed to describe the efficacy of three-dimensional (3D)-printed hollow vascular models as a tool in the preoperative simulation of endovascular embolization of visceral artery aneurysms. METHODS:From November 2015 to November 2016, four consecutive endovascular treatments of true visceral artery aneurysms were preoperatively simulated with 3D-printed hollow models. The mean age of the patients (one male and three females) was 54 (range: 40-71) years. Three patients presented with splenic artery aneurysm and one with anterior pancreaticoduodenal artery aneurysm. The average diameter of the aneurysms was 16.5 (range: 10-25) mm. The 3D-printed hollow models of the visceral artery aneurysms and involved arteries were created using computed tomography angiography data of the patients. After establishing treatment plans by simulations with the 3D-printed models, all patients received endovascular treatment. RESULTS:All four hollow aneurysm models were successfully fabricated and used in the preoperative simulation of endovascular treatment. In the preoperative simulations with 3D-printed hollow models, splenic aneurysms were embolized with coils and/or n-butyl-2-cyanoacrylate to establish the actual treatment plans, and a small arterial branch originating from an anterior pancreaticoduodenal artery aneurysm was selected to obtain feedback regarding the behavior of catheters and guidewires. After establishing treatment plans by simulations, the visceral artery aneurysms of all patients were successfully embolized without major complications and recanalization. CONCLUSIONS:Simulation with 3D-printed hollow models can help establish an optimal treatment plan and may improve the safety and efficacy of endovascular treatment for visceral artery aneurysms.

SUBMITTER: Shibata E

PROVIDER: S-EPMC7294531 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Embolization of visceral arterial aneurysms: Simulation with 3D-printed models.

Shibata Eisuke E Takao Hidemasa H Amemiya Shiori S Ohtomo Kuni K Abe Osamu O

Vascular 20200119 3

<h4>Objectives</h4>The present technical article aimed to describe the efficacy of three-dimensional (3D)-printed hollow vascular models as a tool in the preoperative simulation of endovascular embolization of visceral artery aneurysms.<h4>Methods</h4>From November 2015 to November 2016, four consecutive endovascular treatments of true visceral artery aneurysms were preoperatively simulated with 3D-printed hollow models. The mean age of the patients (one male and three females) was 54 (range: 40 ...[more]

PMID: 31955665

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Project description:BackgroundThe purpose of this study was to find a utility of a newly developed 3D-printed sinus model and to evaluate the educational benefit of simulation training with the models for functional endoscopic sinus surgery (FESS).Material and methodsForty-seven otolaryngologists were categorized as experts (board-certified physicians with ≥200 experiences of FESS, n = 9), intermediates (board-certified physicians with <200 experiences of FESS, n = 19), and novices (registrars, n = 19). They performed FESS simulation training on 3D-printed models manufactured from DICOM images of computed tomography (CT) scan of real patients. Their surgical performance was assessed with the objective structured assessment of technical skills (OSATS) score and dissection quality evaluated radiologically with a postdissection CT scan. First we evaluated the face, content, and constructive values. Second we evaluated the educational benefit of the training. Ten novices underwent training (training group) and their outcomes were compared to the remaining novices without training (control group). The training group performed cadaveric FESS surgeries before and after the repetitive training.ResultsThe feedback from experts revealed high face and content value of the 3D-printed models. Experts, intermediates, and novices demonstrated statistical differences in their OSATS scores (74.7 ± 3.6, 58.3 ± 10.1, and 43.1 ± 11.1, respectively, p < .001), and dissection quality (81.1 ± 13.1, 93.7 ± 15.1, and 126.4 ± 25.2, respectively, p < .001). The training group improved their OSATS score (41.1 ± 8.0 to 61.1 ± 6.9, p < .001) and dissection quality (122.1 ± 22.2 to 90.9 ± 10.3, p = .013), while the control group not. After training, 80% of novices with no prior FESS experiences completed surgeries on cadaver sinuses.ConclusionRepeated training using the models revealed an initial learning curve in novices, which was confirmed in cadaveric mock FESS surgeries.Level of evidenceN/A.

Project description:Background and aimsIntestinal anastomosis is a clinical procedure widely used to reconstruct the digestive tract, but authentic laparoscopic intracorporeal intestinal anastomosis (LIIA) models are lacking. However, three-dimensional (3D) printing can enable authentic and reusable models. In this paper, a novel cost-effective 3D-printing training model of LIIA is designed and the authenticity and validity of the model are tested.MethodsA fused deposition modeling 3D printing and an assembled lab model were built to test LIIA. Fifteen surgeons were required to perform LIIA, and their operation score and time were recorded and analyzed. Five experts were invited to assess the face and content validity of the models. A study was also performed to further evaluate and validate the learning curve of surgeons.ResultsThe difference in modified anastomosis objective structured assessment of technical skills (MAOSATS) scores between the expert, intermediate, and novice groups were significant (64.1±1.8: 48.5±1.7: 29.5±3.1, P <0.001). In addition, the operation time of the procedure was statistically different for all three groups (21.5±1.9: 30.6±2.8:70.7±4.0, P<0.001 ). The five experts rated the face and content validity of the model very highly, with the median being four out of five. Surgeons who underwent repeated training programs showed improved surgical performance. After eight training sessions, the novices' performance was similar to that of the average level of untrained intermediates, while the operation scores of the intermediates were close to that of the average level of experts.ConclusionsIn this study, it is found that the LIIA model exhibits excellent face, content, and construct validity. Repeated simulation training of the LIIA training program improved the surgeon's operative performance, so the model is considered one of the most effective methods for LIIA training and assessment of surgical quality in the future and for reducing healthcare costs.

Project description:ObjectiveThe objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function.MethodsThis retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis.Results5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance.ConclusionThis study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

Dataset Information

Embolization of visceral arterial aneurysms: Simulation with 3D-printed models.

Publications

Embolization of visceral arterial aneurysms: Simulation with 3D-printed models.

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