Artificial Intelligence Technologies in Cardiology

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Core characteristics of sublingual veins analysis and its relationship with hypertension

BACKGROUND

The sublingual vein (SV) is a specialized diagnostic method used in Traditional Chinese Medicine (TCM). Despite its ability to objectively reflect blood flow, SV is often overlooked in clinical practice.

OBJECTIVE

This study aims to analyze the core characteristics of SV and investigate the in-depth relationship between its digital characteristics and hypertension. The goal is to find a link between SV and hypertension and break out of the current situation.

METHODS

Modern digital analysis techniques were applied to the traditional SV diagnostic theory. In a controlled study with 204 participants, the digital characteristics of SV were documented using TFDA-1, and its color value was analyzed using TDAS. Morphological characteristics of SV, such as trunklength, width, and tortuosity, were examined by combining computer vision with expert interpretation. This involved the application of automatic ranging methods and a rectangular approximation algorithm, which are novel approaches in the field of TCM. The t-test and Mann-Whitney U test were used to analyze the digital characteristics of SV in hypertension. Binary logistic regression and neural network models were established using machine learning to explore the deep relationship between SV characteristics and hypertension.

RESULTS

There was a significant difference of the tortuosity of SV between the two groups (Z=-2.629, p= 0.009). The results revealed thick width of SV (OR = 2.64, 95% CI: 1.02-6.79) was the risk factor for hypertension. Addition of SV characteristics improved overall percent correct for hypertension prediction to 80%.

CONCLUSION

TCM method of diagnosis of SV has been greatly expanded in terms of technical means, and the close relationship between SV and hypertension has been found in clinical data.

Clinical Characteristics and Outcomes of Aortic Arch Emergencies: Takayasu Disease, Fibromuscular Dysplasia, and Aortic Arch Pathologies: A Retrospective Study and Review of the Literature

Non-atherosclerotic aortic arch pathologies (NA-AAPs) and anatomical variants are characterized as rare cardiovascular diseases with a low incidence rate, below 1 case per 2000 population, but enormous heterogeneity in terms of anatomical variants, i.e., Takayasu disease (TAK) and fibromuscular dysplasia (FMD). In specific clinical scenarios, NA-AAPs constitute life-threatening disorders.

METHODS

In this study, 82 (1.07%) consecutive patients with NA-AAPs (including 38 TAKs, 26 FMDs, and 18 other AAPs) out of 7645 patients who underwent endovascular treatment (EVT) for the aortic arch and its side-branch diseases at a single institution between 2002 and 2022 were retrospectively reviewed. The recorded demographic, biochemical, diagnostic, operative, and postoperative factors were reviewed, and the functional outcomes were determined during follow-up. A systematic review of the literature was also performed.

RESULTS

The study group comprised 65 (79.3%) female and 17 (21.7%) male subjects with a mean age of 46.1 ± 14.9 years. Overall, 62 (75.6%) patients were diagnosed with either cerebral ischemia symptoms or aortic arch dissection on admission. The EVT was feasible in 59 (72%) patients, whereas 23 (28%) patients were referred for medical treatment. In EVT patients, severe periprocedural complications occurred in two (3.39%) patients, including one periprocedural death and one cerebral hyperperfusion syndrome. During a median follow-up period of 64 months, cardiovascular events occurred in 24 (29.6%) patients (5 deaths, 13 ISs, and 6 myocardial infarctions). Repeated EVT for the index lesion was performed in 21/59 (35.6%) patients, including 19/33 (57.6%) in TAK and 2/13 (15.4%) in FMD. In the AAP group, one patient required additional stent-graft implantation for progressing dissection to the iliac arteries at 12 months. A baseline white blood count (odds ratio [HR]: 1.25, 95% confidence interval [CI]: 1.11-1.39; p < 0.001) was the only independent prognostic factor for recurrent stenosis, while a baseline hemoglobin level (HR: 0.73, 95%CI: 0.59-0.89; p = 0.002) and coronary involvement (HR: 4.11, 95%CI: 1.74-9.71; p = 0.001) were independently associated with a risk of major cardiac and cerebral events according to the multivariate Cox proportional hazards regression analysis.

CONCLUSIONS

This study showed that AAPs should not be neglected in clinical settings, as it can be a life-threatening condition requiring a multidisciplinary approach. The knowledge of prognostic risk factors for adverse outcomes may improve surveillance in this group of patients.

The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.