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Abstract Background Percutaneous coronary interventions (PCI) of bifurcation stenoses are both complex and challenging. Stenting strategies share that the stents’ side cells must be carefully explored and appropriately prepared using balloons or stents. So far, stent manufacturers have not provided any information regarding side-branch expansion capacity of their stent platforms. Aims Given that drug-eluting stent (DES) information regarding their mechanical capacity of side-branch expansion is not available, we aimed to evaluate contemporary DES (Orsiro, BIOTRONIK AG; Xience Sierra, Abbott Vascular; Resolute Integrity, Medtronic; Promus Premier Select, Boston Scientific; Supraflex Cruz, Sahajan and Medical Technologies) by their side-branch expansion behavior using in vitro bench testing. Methods In this in vitro study, we analyzed five commercially available DES (diameter 3.0 mm), measuring their side-branch expansion following inflation of different high-pressure non-compliant (NC) balloons (balloon diameter: 2.00–4.00 mm), thereby revealing the morphological characteristics of their side-branch expansion capacities. Results We demonstrated that all tested contemporary DES platforms could withstand large single-cell deformations, up to 4.0 mm. As seen in our side-branch experiments, DES designs consisting of only two connectors between strut rings did not only result in huge cell areas, but also in larger cell diameters following side-branch expansion compared with DES designs using three or more connectors. Furthermore, the stent cell diameter attained was below the balloon diameter at normal pressure. Conclusions We recommend that the expansion capacity of side-branches should be considered in stent selection for bifurcation interventions.

Abstract Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.