3.8 Editorial Material

Editorial: Manual Versus Automated Methods of IVUS Analysis - The Future of Core Laboratory Appears Gloomy!

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Summary: Intravascular imaging plays an important role in assessing the effectiveness of drug therapies targeting atherosclerosis. Serial intravascular ultrasound, optical coherence tomography, or near-infrared spectroscopy-intravascular ultrasound studies allow for evaluation of the impact of different treatments on plaque burden and morphology. However, established intravascular imaging surrogate endpoints have limited efficacy in predicting the prognostic value of these therapies, necessitating the development of alternative imaging endpoints based on multimodality/hybrid intravascular imaging.

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Effect of High-Intensity Statin Therapy on Atherosclerosis (IBIS-4): Manual Versus Automated Methods of IVUS Analysis

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Summary: The aim of this study is to determine if the results from a core laboratory (CL) can be replicated with a machine learning (ML) algorithm. The study found that ML algorithm can effectively reproduce the analysis results of the CL.

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Summary: This study utilizes a machine learning classifier combined with IVUS echogenicity and NIRS signal to classify plaque components, with histology as the reference standard. The results show that the combination of NIRS signal and echogenicity helps overcome the limitations of echogenicity and improves the accuracy of plaque component classification.

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Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

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Summary: This study developed a deep learning method for automatic segmentation of IVUS images, including stent area along with lumen and vessel area. The DL-based segmentation showed good correlation and agreement with manual segmentation by experts, indicating the feasibility of AI-assisted IVUS assessment in patients undergoing coronary stent implantation.

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