JNC: 2022 Artificial intelligence primer for the nuclear cardiologist (Nov/Dec 2023)-2022 Artificial intelligence primer for the nuclear cardiologist

2022 Artificial intelligence primer for the nuclear cardiologist

Pdf Summary

Artificial Intelligence (AI) has the potential to revolutionize the field of nuclear cardiology by enhancing every aspect of its workflow, from image acquisition to interpretation. In a comprehensive review, Dr. Manish Motwani provides a primer on AI for nuclear cardiologists in 2022, explaining key terminology and summarizing current implementations, challenges, and future aspirations of AI in nuclear cardiology.<br /><br />AI, a subset of AI, uses computational techniques to mimic human thought processes and learning capacity. Machine Learning (ML) is a type of AI that enables computer algorithms to learn and make predictions from data without explicit programming. Deep Learning (DL) is a type of ML that uses multi-layered neural networks to directly generate predictions from input data.<br /><br />One area where AI can enhance nuclear cardiology is in image segmentation, which involves dividing an image into anatomical or clinically meaningful parts. AI-based techniques, such as CNNs, have demonstrated excellent precision in segmenting the left ventricle and the mitral valve plane.<br /><br />AI can also improve the diagnosis of coronary artery disease (CAD) by integrating clinical and imaging data to optimize diagnostic accuracy. ML algorithms have shown superior performance in predicting CAD compared to conventional imaging measures and expert visual analysis.<br /><br />Furthermore, AI can aid in risk stratification by combining clinical and imaging-derived numerical data to predict major adverse cardiovascular events. ML models have demonstrated higher accuracy in predicting future events compared to conventional methods.<br /><br />Other applications of AI in nuclear cardiology include image acquisition, reconstruction, and denoising. DL-based algorithms have shown potential in reducing radiation dose, acquisition time, and improving image quality. Additionally, AI can be used in attenuation correction to improve diagnostic accuracy and identify and quantify cardiovascular information.<br /><br />While AI has tremendous potential in nuclear cardiology, robust validation in large real-world datasets is needed before widespread clinical implementation. Nonetheless, AI has the potential to enhance the precision and efficiency of nuclear cardiology, providing clinicians with valuable tools for diagnosis, risk stratification, and treatment planning.

Keywords

Artificial Intelligence

nuclear cardiology

image acquisition

interpretation

Machine Learning

Deep Learning

image segmentation

coronary artery disease

diagnostic accuracy

risk stratification