Prediction of Heart and Liver Iron Overload in β-Thalassemia Major Patients Using Machine Learning Methods

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography

Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology.

Cardiovascular magnetic resonance in β-thalassemia major: beyond T2

Β-thalassemia major (TM) patients underwent regular transfusions to prevent complications of chronic anemia. However, these regular transfusions result in progressive iron accumulation in vital organs, including the heart. Myocardial iron overload can lead to cardiac dysfunction and ultimately to heart failure. Diagnosis of cardiac dysfunction in β-TM patients is usually made through clinical examination, electrocardiogram, and echocardiography. Cardiac magnetic resonance (CMR), through the measurement of T2* relaxation time, represents the diagnostic modality of choice for assessing myocardial iron overload and guiding the iron chelation therapy. Despite a tailored chelation therapy reducing myocardial iron overload, heart failure remains the leading cause of morbidity and mortality even in well-treated β-TM patients. Advances in CMR, including myocardial strain, parametric mapping (T1, T2, and extracellular volume), and late gadolinium enhancement (LGE) measurements, have expanded its role in the diagnosis, prognosis, and follow-up of these patients. This review seeks to offer a thorough overview of the potential uses of CMR in β-TM, extending beyond the established role of T2* measurement in guiding chelation therapy. It delves into the emerging applications of new CMR imaging biomarkers that could improve the overall management of β-TM patients.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.