Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

4D Light-sheet imaging and interactive analysis of cardiac contractility in zebrafish larvae

Despite ongoing efforts in cardiovascular research, the acquisition of high-resolution and high-speed images for the purpose of assessing cardiac contraction remains challenging. Light-sheet fluorescence microscopy (LSFM) offers superior spatiotemporal resolution and minimal photodamage, providing an indispensable opportunity for the in vivo study of cardiac micro-structure and contractile function in zebrafish larvae. To track the myocardial architecture and contractility, we have developed an imaging strategy ranging from LSFM system construction, retrospective synchronization, single cell tracking, to user-directed virtual reality (VR) analysis. Our system enables the four-dimensional (4D) investigation of individual cardiomyocytes across the entire atrium and ventricle during multiple cardiac cycles in a zebrafish larva at the cellular resolution. To enhance the throughput of our model reconstruction and assessment, we have developed a parallel computing-assisted algorithm for 4D synchronization, resulting in a nearly tenfold enhancement of reconstruction efficiency. The machine learning-based nuclei segmentation and VR-based interaction further allow us to quantify cellular dynamics in the myocardium from end-systole to end-diastole. Collectively, our strategy facilitates noninvasive cardiac imaging and user-directed data interpretation with improved efficiency and accuracy, holding great promise to characterize functional changes and regional mechanics at the single cell level during cardiac development and regeneration.

Light sheet imaging and interactive analysis of the cardiac structure in neonatal mice

Light-sheet microscopy (LSM) enables us to strengthen the understanding of cardiac development, injury, and regeneration in mammalian models. This emerging technique decouples laser illumination and fluorescence detection to investigate cardiac micro-structure and function with a high spatial resolution while minimizing photodamage and maximizing penetration depth. To unravel the potential of volumetric imaging in cardiac development and repair, we sought to integrate our in-house LSM, Adipo-Clear, and virtual reality (VR) with neonatal mouse hearts. We demonstrate the use of Adipo-Clear to render mouse hearts transparent, the development of our in-house LSM to capture the myocardial architecture within the intact heart, and the integration of VR to explore, measure, and assess regions of interests in an interactive manner. Collectively, we have established an innovative and holistic strategy for image acquisition and interpretation, providing an entry point to assess myocardial micro-architecture throughout the entire mammalian heart for the understanding of cardiac morphogenesis.

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provide an explanation or rationale for model outcomes. Complexity and vagueness in these models necessitate a transition to explainable artificial intelligence (XAI) methods to ensure that model results are both transparent and understandable to end users. In cardiac imaging studies, there are a limited number of papers that use XAI methodologies. This article provides a comprehensive literature review of state-of-the-art works using XAI methods for cardiac imaging. Moreover, it provides simple and comprehensive guidelines on XAI. Finally, open issues and directions for XAI in cardiac imaging are discussed.