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Background: Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear., Objectives: To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up., Methods and Results: We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 μm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 μm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups., Results: In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001., Conclusion: In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices., Competing Interests: Declaration of competing interest Ron Waksman: Advisory Board: Amgen, Boston Scientific, Cardioset, Cardiovascular Systems Inc., Medtronic, Philips, Pi-Cardia Ltd.; Consultant: Amgen, Biotronik, Boston Scientific, Cardioset, Cardiovascular Systems Inc., Medtronic, Philips, Pi-Cardia Ltd.; Grant Support: AstraZeneca, Biotronik, Boston Scientific, Chiesi; Speakers Bureau: AstraZeneca, Chiesi; Investor: MedAlliance. All other authors have no conflicts of interest to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Aims: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods., Methods: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm., Results: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of -0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of -1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of -2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of -3.74 % (p < 0.001)., Conclusions: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard., Competing Interests: Declaration of competing interest Héctor M. García-García reports the following Institutional grant support: Biotronik, Boston Scientific, Medtronic, Abbott, Neovasc, Shockwave, Phillips and Corflow; Speaker's fee from Boston Scientific and Amgen. Lorenz Räber reports institutional grant support: Abbott, Biotronik, Boston Scientific, Sanofi, Regeneron, Infraredx and consultation fees by Abbott, Amgen, AstraZeneca, Occlutec, Medtronic, Sanofi, Vifor. Marco Roffi reports institutional research grants from Terumo, Biotronik, Medtronic and Cordis/Cardinal Health., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm 2 , [1.1,-1.5]mm 2 , while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest with regards to the present work., (Copyright © 2021. Published by Elsevier B.V.)

Background: Geometrical risk factors for CAD have been previously proposed before. To date, however, the effect of those factors is not conclusive, and remains as an open research field. Here, we hypothesize that some of these factors have a genetic component explaining inter-individual variability., Objective: To detect heritability indicators of the coronary arterial geometry., Materials and Methods: A patient sample of 48 individuals, consisting of 24 siblings, was used. Three dimensional geometry of the LAD, LCx and RCA were reconstructed from standard CCTA. Arterial models were characterized in terms of 20+ geometric descriptors (phenotypes). A comprehensive statistical analysis to detect potential heritability of such phenotypes was employed. Heritability was assessed by means of several statistical indexes. Finally, the association of phenotypes to stenotic lesion is also reported., Results: The RCA scored positive indications for heritability in 15+ phenotypes, while the LAD in 10 and the LCx in only 3 phenotypes. Association between presence of lesion and phenotypes was higher in the LAD, 10+ phenotypes, while for the LCx only 2 phenotypes were significantly associated, and none association was found for the RCA., Conclusion: The RCA showed potential heritability for the largest number of phenotypes, followed by the LAD. The LCx presents the weaker association of morphology among siblings. Regarding lesion-geometry associations, the there are hints of an underlying relation in the LAD, the LCx featured a weaker association and the RCA showed none. This difference could be related to the different hemodynamic environments in these arteries., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)