Integrating Artificial Intelligence in the Pathology of Major Cardiovascular Diseases: From Atherosclerosis to Heart Failure View PDF

CVD remains the leading cause of morbidity and mortality worldwide, accounting for nearly 18 million deaths annually [1, 2]. These diseases encompass a broad spectrum of conditions, including atherosclerosis, coronary artery disease (CAD), heart failure, arrhythmias, and valvular disorders. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes [3, 4]. However, conventional diagnostic techniques in pathology, such as histopathology, molecular analysis, and imaging, often rely on manual interpretation, which can be time-consuming, prone to human error, and limited by inter-observer variability [5]. With the advent of AI, particularly machine learning and deep learning algorithms, the landscape of CVD pathology is undergoing a transformative shift [6, 7]. AI offers the potential to enhance diagnostic accuracy, improve prognostic assessments, and streamline pathological workflows by identifying complex patterns and subtle changes that may elude the human eye [8].

The integration of AI into CVD pathology is driven by its ability to analyze vast amounts of data from diverse sources, including digital pathology slides, genomic datasets, and multi-modal imaging (Table 1) [9]. AI-powered tools can automate tissue classification, detect biomarkers, and predict disease progression with remarkable precision. Moreover, AI models can assist in personalized medicine by correlating pathological findings with clinical data to optimize treatment strategies [10, 11]. Despite its promising potential, the adoption of AI in CVD pathology also presents challenges, including data standardization, validation, and the need for interpretability of AI-generated outputs (Figure 1) [12]. Nevertheless, as technology advances, AI is poised to play an increasingly vital role in the early detection, classification, and management of major CVDs, ultimately improving patient care and clinical outcomes [13].

The integration of AI into the pathology of major CVDs represents a transformative approach in the field of medicine. AI technologies, particularly machine learning and deep learning, have shown significant promise in enhancing diagnostic accuracy, treatment planning, and disease prevention [14, 15]. This review explores the various applications of AI in cardiovascular pathology, highlighting its potential to revolutionize patient care and improve outcomes. The integration of AI in the pathology of major CVDs has been a topic of interest in recent literature. Opincariu et al. [16] discussed the role of coronary computed tomography angiography (CCTA) in detecting and quantifying vulnerable plaques, emphasizing the potential of radiomics-based machine learning for assessing plaqueassociated risk. Sun et al. [17] focused on using machine learning to predict CVDs based on existing functional dependencies. Slart et al. [18] highlighted the application of machine learning algorithms, including deep learning, in cardiovascular imaging to enhance diagnosis and prognostication for patients with CVDs. Hahn et al. [19] explored the foundational concepts of AI and its recent applications in aortic disease, including the analysis of flow dynamics with advanced imaging techniques.

Furthermore, Wang et al. [20] discussed the use of medical knowledge graphs in cardiology and cardiovascular medicine, emphasizing their importance in effective clinical decision-making. Alqahtani et al. [21] proposed an ensemble-based approach using machine learning and deep learning models for CVD detection, considering multiple health variables. Nedadur et al. [22] highlighted the potential of AI in echocardiographic assessment of valvular heart disease, focusing on image acquisition and automated analysis. Lareyre et al. [23] discussed AI-based predictive models in vascular diseases, while Armoundas et al. [24] emphasized the importance of AI tools in improving outcomes in CVDs, although challenges remain in their widespread adoption. Overall, the literature review indicates a growing interest in integrating AI into the pathology of major CVDs, with a focus on enhancing diagnostic accuracy, prognostication, and treatment decision-making through advanced machine learning algorithms and imaging techniques.