Qualitative and Quantitative Stress Perfusion Cardiac Magnetic Resonance in Clinical Practice: A Comprehensive Review

Zhou, Wenli; Sin, Jason; Yan, Andrew T.; Wang, Haonan; Lu, Jing; Li, Yuehua; Kim, Paul J.; Patel, Amit R.; Ng, Ming-Yen

doi:10.3390/diagnostics13030524

Cited by 6 publications

(1 citation statement)

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“…MRI/CMR images provide precise measurements of heart structure and functions, as well as myocardial perfusion and parametric quantification. MRI/CMR could be 2D or 3D, but 3D imaging has excessive artifacts and has thus not been clinically used for the diagnosis of CAD [ 8 ]. Manual interpretation of 2D scans is also time-consuming and requires experience.…”

Section: Introductionmentioning

confidence: 99%

Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging

Iqbal,

Khalid,

Ullah

2024

J Real-Time Image Proc

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In certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis and decision-making are required, the healthcare system can leverage the power of artificial intelligence (AI) models to support decision-making and prevent complications. This paper investigates the optimization of healthcare AI models based on time complexity, hyper-parameter tuning, and XAI for a classification task. The paper highlights the significance of a lightweight convolutional neural network (CNN) for analysing and classifying Magnetic Resonance Imaging (MRI) in real-time and is compared with CNN-RandomForest (CNN-RF). The role of hyper-parameter is also examined in finding optimal configurations that enhance the model’s performance while efficiently utilizing the limited computational resources. Finally, the benefits of incorporating the XAI technique (e.g. GradCAM and Layer-wise Relevance Propagation) in providing transparency and interpretable explanations of AI model predictions, fostering trust, and error/bias detection are explored. Our inference time on a MacBook laptop for 323 test images of size 100x100 is only 2.6 sec, which is merely 8 milliseconds per image while providing comparable classification accuracy with the ensemble model of CNN-RF classifiers. Using the proposed model, clinicians/cardiologists can achieve accurate and reliable results while ensuring patients’ safety and answering questions imposed by the General Data Protection Regulation (GDPR). The proposed investigative study will advance the understanding and acceptance of AI systems in connected healthcare settings.

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Section: Introductionmentioning

confidence: 99%