Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

Background: Pectus excavatum is the most common chest wall deformity. There is still controversy about cardiopulmonary limitations of this disease and benefits of surgical repair. This study evaluates the impact of pectus excavatum on the cardiopulmonary function of adult patients before and after a modified minimally invasive repair. Methods and Results: In this retrospective cohort study, an electronic database was used to identify consecutive adult (aged ≥18 years) patients who underwent cardiopulmonary exercise testing before and after primary pectus excavatum repair at Mayo Clinic Arizona from 2011 to 2020. In total, 392 patients underwent preoperative cardiopulmonary exercise testing; abnormal oxygen consumption results were present in 68% of patients. Among them, 130 patients (68% men, mean age, 32.4±10.0 years) had post‐repair evaluations. Post‐repair tests were performed immediately before bar removal with a mean time between repair and post‐repair testing of 3.4±0.7 years (range, 2.5–7.0). A significant improvement in cardiopulmonary outcomes ( P <0.001 for all the comparisons) was seen in the post‐repair evaluations, including an increase in maximum, and predicted rate of oxygen consumption, oxygen pulse, oxygen consumption at anaerobic threshold, and maximal ventilation. In a subanalysis of 39 patients who also underwent intraoperative transesophageal echocardiography at repair and at bar removal, a significant increase in right ventricle stroke volume was found ( P <0.001). Conclusions: Consistent improvements in cardiopulmonary function were seen for pectus excavatum adult patients undergoing surgery. These results strongly support the existence of adverse cardiopulmonary consequences from this disease as well as the benefits of surgical repair.

Management of adults with coarctation of aorta

Aims: Lipoprotein(a) (Lp(a)) levels are known to be mainly genetically determined. However, only scarce data are available on the intra-individual variability of Lp(a) levels across time. Methods and results: We included adult patients (≥18 years old) who had baseline and follow-up Lp(a) measurements (between 1997 and 2024) with a minimum of 1 year apart. Patients were categorized into three groups as follows: normal (<30 mg/dL), borderline (30 to 50 mg/dL), and high Lp(a) (≥50 mg/dL). Multivariable logistic regression was conducted to assess the predictors of the intra-individual changes in Lp(a) ≥ 10 mg/dL. A total of 11 669 individuals (median age: 54 years, 60% males) were included in our analysis, with median time between measurements of 4.5 years [interquartile range (IQR): 2.2, 10.6]. The median Lp(a) was 16 mg/dL (IQR: 7, 52) at baseline, compared with 15 mg/dL (IQR: 7, 52) at follow-up. At follow-up, 96.4% of individuals with normal Lp(a) and 89.9% with high Lp(a) remained in their categories, while 51.2% with borderline Lp(a) changed their category. Of the included population, 24.9% had an intra-individual Lp(a) change ≥ 10 mg/dL. Female sex (P < 0.001), history of ASCVD (P = 0.003), statin therapy (P = 0.003), and elevated LDL cholesterol (LDL-C) levels ≥ 100 mg/dL (P < 0.001) were significantly associated with higher odds of intra-individual Lp(a) changes ≥ 10 mg/dL. Conclusion: Lipoprotein(a) levels were generally stable over time; however, patients with borderline levels may require more than one Lp(a) measurement, especially if they are females, have a history of ASCVD, have elevated LDL-C levels, or are on statin therapy.