Bioscience Methods 2024, Vol.15, No.3, 124-138 http://bioscipublisher.com/index.php/bm 136 overcome existing challenges and fully realize the potential of these innovations in improving cardiovascular health outcomes. 10 Concluding Remarks The development of AI-based diagnostic systems for hypertensive heart disease has shown significant promise across various studies. AI applications have demonstrated high diagnostic accuracy in identifying hypertension and related cardiovascular conditions using diverse data sources such as electrocardiograms (ECGs), cardiac MRI, and wearable devices. For instance, AI algorithms have been effective in predicting pulmonary hypertension (PH) using ECGs with high accuracy, achieving an area under the receiver operating characteristic curve (AUC) of up to 0.902 in external validation. Additionally, AI models have been developed to detect hypertension and stratify cardiovascular risk from 12-lead ECGs, showing strong associations with incident cardiovascular events. Moreover, AI applied to cross-sectional imaging, such as cardiac MRI, has demonstrated high diagnostic accuracy for pulmonary arterial hypertension (PAH), with AUC values reaching 0.97. These findings underscore the potential of AI to enhance the early detection and management of hypertensive heart disease. The integration of AI-based diagnostic systems into clinical practice could revolutionize the management of hypertensive heart disease. AI algorithms can provide continuous monitoring and personalized treatment plans, improving patient outcomes by enabling early diagnosis and timely intervention. For example, AI systems can utilize wearable technologies to monitor blood pressure (BP) continuously, offering a more accurate and real-time assessment of hypertensive status. Furthermore, AI's ability to analyze large datasets and identify patterns can help in the early detection of hypertension-related complications, such as heart failure and myocardial infarction, thereby reducing morbidity and mortality. The use of AI in clinical settings can also alleviate the burden on healthcare professionals by providing decision support, thus enhancing the efficiency and accuracy of diagnoses. Despite the promising advancements, several challenges remain that warrant further research. Future studies should focus on addressing the technical issues and biases associated with AI algorithms, such as overfitting, the "black-box" nature of machine learning models, and patient data privacy concerns. Additionally, there is a need for larger, more diverse datasets to validate AI models across different populations and clinical settings. Research should also explore the integration of AI with omics-based technologies to provide a comprehensive understanding of hypertensive heart disease and its progression. Moreover, the development of explainable AI models that offer transparent decision-making processes will be crucial for gaining the trust of healthcare providers and patients. Finally, interdisciplinary collaboration between AI researchers, clinicians, and policymakers will be essential to ensure the successful implementation and ethical use of AI in healthcare. Acknowledgments Author extends sincere thanks to two anonymous peer reviewers for their feedback on the manuscript. Conflict of Interest Disclosure Author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Reference Al-Alusi M., Friedman S., Kany S., Ramo J., Pipilas D., Singh P., Reeder C., Khurshid S., Pirruccello J., Maddah M., Ho J., and Ellinor P., 2023, Abstract 13901: deep learning-based digital biomarker to diagnose hypertension and stratify cardiovascular risk from the electrocardiogram, Circulation, 10: 2. https://doi.org/10.1161/circ.148.suppl_1.13901 Ali L., Rahman A., Khan A., Zhou M., Javeed A., and Khan J., 2019, An automated diagnostic system for heart disease prediction based on statistical model and optimally configured deep neural network, IEEE Access, 7: 34938-34945. https://doi.org/10.1109/ACCESS.2019.2904800 Alizadehsani R., Khosravi A., Roshanzamir M., Abdar M., Sarrafzadegan N., Shafie D., Khozeimeh F., Shoeibi A., Nahavandi S., Panahiazar M., Bishara A., Beygui R., Puri R., Kapadia S., Tan R., and Acharya U., 2020, Coronary artery disease detection using artificial intelligence techniques: a survey of trends, geographical differences and diagnostic features 1991-2020, Computers in Biology and Medicine, 128: 104095. Angelaki E., Barmparis G., Kochiadakis G., Maragkoudakis S., Savva E., Kampanieris E., Kassotakis S., Kalomoirakis P., Vardas P., Tsironis G., and Marketou M., 2022, Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals, Journal of Hypertension, 40: 2494-2501.
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