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Background: Endovascular treatment has become the first-line strategy for peripheral arterial disease (PAD). Given the number of procedures required, any technology associated with a reduction in radiation exposure and contrast volume is highly relevant. In the present study, we evaluated whether two-dimensional (2D) fusion imaging could reduce the radiation exposure and contrast volume during endovascular treatment of occlusive PAD., Methods: Our consecutive, retrospective, single-center, nonrandomized comparative trial included patients with PAD at the femoral, popliteal, and/or tibial level, at any clinical stage, if they were candidates for endovascular revascularization. Patients were treated with or without the EndoNaut 2D fusion imaging system (Therenva, Rennes, France) in a nonhybrid room with the same Cios Alpha mobile C-arm (Siemens, Munich, Germany). The indirect dose-area product and contrast medium volume were recorded., Results: Between March 2018 and April 2020, 255 patients underwent endovascular femoropopliteal revascularization with (n = 124) or without (n = 131) 2D fusion imaging. The volume of injected contrast medium (34.7 ± 13.8 mL vs 51.3 ± 26.7 mL; P < .001) and dose-area product (8.9 ± 9.9 Gy/cm 2 vs 13.5 ± 14.0 Gy/cm 2 ; P = .003) were significantly lower for the 2D fusion imaging group than for the control group. A subgroup analysis of complex (TransAtlantic Inter-Society Consensus for the Management of Peripheral Arterial Disease C/D) lesions showed similar results. Stratification of the fusion imaging group into three subgroups, according to the procedure dates, showed no effect of a potential learning curve on the operative parameters., Conclusions: The results from the present study showed a significant reduction in the contrast volume and radiation dose for endovascular treatment of PAD when applying 2D fusion imaging technology. Overall, a reduction of >30% was observed for both operative parameters, without excessive training requirements, highlighting the potential benefits of using 2D fusion imaging when performing endovascular revascularization for PAD., (Copyright © 2021 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures., Competing Interests: The authors have declared that no competing interests exist. FL as a commercial affiliation with Therenva. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: Sac shrinkage is considered a reliable surrogate marker of success after endovascular aneurysm repair (EVAR). Whereas sac shrinkage is the best expected outcome, predictive factors of sac shrinkage remain unclear. The aim of this study was to identify the role of preoperative and postoperative influencing factors of sac reduction after EVAR., Methods: Online searches across MEDLINE, Embase, and Cochrane Library medical databases were simultaneously performed. Study effects were pooled using a random-effects model, and forest plots were generated for every potential influencing factor., Results: A total of 24 studies with 14,754 patients were included (mean age, 73.4 years; 76% male). At a mean follow-up of 24 months, the pooled shrinkage proportion was 47%. Random-effects meta-analysis revealed that renal impairment (odds ratio [OR], 0.74; 95% confidence interval [CI], 0.57-0.96), type I endoleaks (OR, 0.17; 95% CI, 0.08-0.39), type II endoleaks (OR, 0.21; 95% CI, 0.14-0.33), and combined type I and type II endoleaks (OR, 0.32; 95% CI, 0.22-0.47) were found to prevent sac shrinkage, whereas hypercholesterolemia (OR, 1.24; 95% CI, 1.02-1.51) and smoking (OR, 1.32; 95% CI, 1.17-1.49) have a significant positive impact on sac shrinkage. In addition, there was a trend toward the association between shrinkage and statin therapy (OR, 4.07; 95% CI, 1.02-16.32) and nearly significant negative impacts of coronary artery disease (OR, 0.84; 95% CI, 0.70-1.01), diabetes (OR, 0.79; 95% CI, 0.60-1.04), and sac thrombus (OR, 0.88; 95% CI, 0.77-1.01) on sac shrinkage., Conclusions: In this large meta-analysis of patients undergoing EVAR, we found that several comorbidity and postoperative factors were associated with postoperative sac shrinkage. These findings may contribute to a better understanding of the shrinkage process of patients undergoing EVAR., (Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre-surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label-fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored., (Copyright © 2014 Wiley Periodicals, Inc.)

Surgical Process Modelling (SPM) was introduced to improve understanding the different parameters that influence the performance of a Surgical Process (SP). Data acquired from SPM methodology is enormous and complex. Several analysis methods based on comparison or classification of Surgical Process Models (SPMs) have previously been proposed. Such methods compare a set of SPMs to highlight specific parameters explaining differences between populations of patients, surgeons or systems. In this study, procedures performed at three different international University hospitals were compared using SPM methodology based on a similarity metric focusing on the sequence of activities occurring during surgery. The proposed approach is based on Dynamic Time Warping (DTW) algorithm combined with a clustering algorithm. SPMs of 41 Anterior Cervical Discectomy (ACD) surgeries were acquired at three Neurosurgical departments; in France, Germany, and Canada. The proposed approach distinguished the different surgical behaviors according to the location where surgery was performed as well as between the categorized surgical experience of individual surgeons. We also propose the use of Multidimensional Scaling to induce a new space of representation of the sequences of activities. The approach was compared to a time-based approach (e.g. duration of surgeries) and has been shown to be more precise. We also discuss the integration of other criteria in order to better understand what influences the way the surgeries are performed. This first multi-site study represents an important step towards the creation of robust analysis tools for processing SPMs. It opens new perspectives for the assessment of surgical approaches, tools or systems as well as objective assessment and comparison of surgeon's expertise., (Copyright © 2013 Elsevier Inc. All rights reserved.)

In the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) are an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Processes (SPs) which are formalized as symbolic structured descriptions of surgical interventions using a pre-defined level of granularity and a dedicated terminology. In this context, one major challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data are used to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to the classical methods that only focus on differences in the time and the number of activities. Experiments were carried out on 24 lumbar disk herniation surgeries to discriminate the surgeons level of expertise according to a prior classification of SPs. Supervised and unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for comparing and evaluating surgeries., (Copyright © 2011. Published by Elsevier Inc.)