A systematic review of 145 methods found that while UNet provides a powerful paradigm for IVUS coronary wall segmentation, none of the models met the criteria for a bias-free design.
Systematic Review (n=145)
While UNet models are powerful for IVUS coronary wall segmentation, current models lack bias-free designs and explainable AI frameworks, highlighting a gap for clinical translation.
BACKGROUND AND MOTIVATION: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
Kumari et al. (Mon,) conducted a systematic review in Coronary artery disease (n=145). Deep learning paradigms (UNet and non-UNet methods) was evaluated on Characteristics, scientific and clinical validation, and bias in deep learning systems for wall segmentation. A systematic review of 145 methods found that while UNet provides a powerful paradigm for IVUS coronary wall segmentation, none of the models met the criteria for a bias-free design.
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