Progression of Calcific Aortic Stenosis Detected by Artificial Intelligence Electrocardiogram

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis

Background: Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) algorithms for AS screening.Methods: We conducted a thorough search of six databases. Several evaluation parameters, such as sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and area under the curve (AUC) value were employed in the diagnostic meta-analysis of AI-based algorithms for AS screening. The AI algorithms utilized diverse data sources including electrocardiograms (ECG), chest radiographs, auscultation audio files, electronic stethoscope recordings, and cardio-mechanical signals from non-invasive wearable inertial sensors.Results: Of the 295 articles identified, 10 studies met the inclusion criteria. The pooled estimates for AI-based algorithms in diagnosing AS were as follows: sensitivity 0.83 (95% CI: 0.81-0.85), specificity 0.81 (95% CI: 0.79-0.84), PLR 4.78 (95% CI: 3.12-7.32), NLR 0.20 (95% CI: 0.13-0.28), and DOR 27.11 (95% CI: 14.40-51.05). The AUC value was 0.909 (95% CI: 0.889-0.929), indicating outstanding diagnostic accuracy. Subgroup and meta-regression analyses showed that continent, type of AS, data source, and type of AI-based method constituted sources of heterogeneity. Furthermore, we demonstrated proof of publication bias for DOR values analyzed using Egger's regression test (P = 0.002) and a funnel plot.Conclusion: Deep learning approaches represent highly sensitive, feasible, and scalable strategies to identify patients with moderate or severe AS.

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

Lp(a) in the Pathogenesis of Aortic Stenosis and Approach to Therapy with Antisense Oligonucleotides or Short Interfering RNA

To date, no medical therapy can slow the progression of aortic stenosis. Fibrocalcific stenosis is the most frequent form in the general population and affects about 6% of the elderly population. Over the years, diagnosis has evolved thanks to echocardiography and computed tomography assessments. The application of artificial intelligence to electrocardiography could further implement early diagnosis. Patients with severe aortic stenosis, especially symptomatic patients, have valve repair as their only therapeutic option by surgical or percutaneous technique (TAVI). The discovery that the pathogenetic mechanism of aortic stenosis is similar to the atherosclerosis process has made it possible to evaluate the hypothesis of medical therapy for aortic stenosis. Several drugs have been tested to reduce low-density lipoprotein (LDL) and lipoprotein(a) (Lp(a)) levels, inflammation, and calcification. The Proprotein Convertase Subtilisin/Kexin type 9 inhibitors (PCSK9-i) could decrease the progression of aortic stenosis and the requirement for valve implantation. Great interest is related to circulating Lp(a) levels as causally linked to degenerative aortic stenosis. New therapies with ASO (antisense oligonucleotides) and siRNA (small interfering RNA) are currently being tested. Olpasiran and pelacarsen reduce circulating Lp(a) levels by 85-90%. Phase 3 studies are underway to evaluate the effect of these drugs on cardiovascular events (cardiovascular death, non-fatal myocardial injury, and non-fatal stroke) in patients with elevated Lp(a) and CVD (cardiovascular diseases). For instance, if a reduction in Lp(a) levels is associated with aortic stenosis prevention or progression, further prospective clinical trials are warranted to confirm this observation in this high-risk population.

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.