Development of machine learning-based malignant pericardial effusion-related model in breast cancer: Implications for clinical significance, tumor immune and drug-therapy

Zhan, Wendi; Hu, Haihong; Hao, Bo; Zhu, Hongxia; Yan, Ting; Zhang, Jingdi; Wang, Siyu; Liu, Saiyang; Zhang, Taolan

doi:10.1016/j.heliyon.2024.e27507

Cited by 1 publication

(1 citation statement)

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“…AI has proven its usefulness in pericardial diseases; from the diagnosis of liquid pericarditis based on ECG [ 193 ] to the measurement of pericardial fluid based on echocardiography [ 194 ], automatic detection and classification of pericarditis using CT images of the chest [ 195 ], and prediction of fluid pericarditis in patients undergoing cardiac stimulation [ 196 ] or in breast cancer patients [ 192 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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