Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness

Association Between Lipoprotein(a) and Dilatation of Different Aortic Segments in Hypertensive Patients

PURPOSE

There are limited reports on the potential link between Lp(a) and ARDM. Thus, we examined the relationship between Lp(a) and ARDM among hypertensive patients.

METHODS

We used echocardiography to measure ARDM in 513 consecutively hospitalized patients. namely, the aortic valve annulus (Ava), sinuses of Valsalva (SV), sinotubular junction (STJ), and ascending aorta (AA) in 513 consecutive inpatients. We also examined the Lp(a), and other laboratory profiles of all participants.

RESULTS

Lp(a) exhibited a positive and independent relationship with the SV diameter (coefficient [β] = 0.330, p = 0.002) and STJ (coefficient [β] = 0.253, p = 0.023), regardless of age, sex, height, or other clinical factors among hypertensive, but not nonhypertensive patients. We also demonstrated that a marked rise in Lp(a) levels was independently associated with SV dilatation (SVD) (OR: 1.006, 95% CI: 1.002-1.009, p = 0.002) and AA dilatation (AAD) (OR: 1.006, 95% CI: 1.000-1.011, p = 0.035) in patients with hypertension. In the subgroup analysis, elevated Lp(a) levels were significantly associated with SV dilatation in all subgroups, and with AAD in males and patients aged 65 years or younger (p < 0.05). The restricted cubic spline analysis indicated a linear association between Lp(a) levels and the risk of both SV and AAD (p < 0.05).

CONCLUSIONS

Herein, we were the first to report that among hypertensive patients, elevated Lp(a) concentrations were intricately linked to the ARDMs at SV and STJ. Moreover, we revealed that the Lp(a) level was a stand-alone indicator of SVD and AAD.

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease

BACKGROUND

Non-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians' experience.

PURPOSE

The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology.

METHODS

The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning.

RESULTS

Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases.

CONCLUSION

We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.

Patients with Bicuspid Aortopathy and Aortic Dilatation

(1) Background: Bicuspid aortic valve (BAV) is the most frequent congenital cardiac disease. Alteration of ascending aorta diameter is a consequence of shear stress alterations due to haemodynamic abnormalities developed from inadequate valve cusp coaptation. (2) Objective: This narrative review aims to discuss anatomical, pathophysiological, genetical, ultrasound, and radiological aspects of BAV disease, focusing on BAV classification related to imaging patterns and flux models involved in the onset and developing vessel dilatation. (3) Methods: A comprehensive search strategy was implemented in PubMed from January to May 2022. English language articles were selected independently by two authors and screened according to the following criteria. (4) Key Contents and Findings: Ultrasound scan is the primary step in the diagnostic flowchart identifying structural and doppler patterns of the valve. Computed tomography determines aortic vessel dimensions according to the anatomo-pathology of the valve. Magnetic resonance identifies hemodynamic alterations. New classifications and surgical indications derive from these diagnostic features. Currently, indications correlate morphological results, dissection risk factors, and genetic alterations. Surgical options vary from aortic valve and aortic vessel substitution to aortic valve repair according to the morphology of the valve. In selected patients, transcatheter aortic valve replacement has an even more impact on the treatment choice. (5) Conclusions: Different imaging approaches are an essential part of BAV diagnosis. Morphological classifications influence the surgical outcome.