Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review

Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review

Blog Article

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis.Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries.However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving.

This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans.Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent Lift Kit biases in artificial intelligence systems for improving diagnostic accuracy.Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy.

Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans.Additionally, it is the first to investigate biases in deep learning (DL) systems that are TEA TREE PURE-CASTILE SOAP associated with IVUS scan wall segmentation.Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation.

Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans.Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration.Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.