Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views.

This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).

Reduced epigenetic age in older adults who volunteer.

BACKGROUND: Volunteering is associated with improved health and well-being outcomes, including a reduced risk of mortality. However, the biological mechanisms underlying the association between volunteering and healthy aging and longevity have not been well-established. We evaluated if volunteering was associated with reduced epigenetic age acceleration in older adults. METHODS: We evaluated associations between volunteering and age acceleration, measured by 13 DNA methylation (DNAm) "epigenetic clocks" in 4011 older adults (Mage=69 years; SDage=10 years) who participated in the Health and Retirement Study. We assessed 9 first-generation clocks (Horvath, Hannum, Horvath Skin, Lin, Garagnani, Vidalbralo, Weidner, Yang, and Bocklandt, which predict chronological age) and 4 second-generation clocks (Zhang, PhenoAge, GrimAge, and DunedinPoAm, which predict future disease or longevity). We quantified the total associations between volunteering and DNAm age acceleration as well as the extent to which these associations might be attributable to potential confounding by individual demographics (e.g., race), social demographics (e.g., income), health factors (e.g., diabetes), and health behaviors (e.g., smoking). RESULTS: Volunteering was associated with reduced epigenetic age acceleration across 6 epigenetic clocks optimized for predicting health and longevity (False Discovery Rate [FDR] q < 0.0001 for epigenetic clocks: PhenoAge, GrimAge, DunedinPoAm, Zhang mortality, Yang mitotic; FDR q < 0.01: Hannum). These associations were mostly independent of demographic and health factors, but substantially attenuated after adjusting for health behaviors. CONCLUSION: Volunteering was associated with reduced epigenetic age acceleration in 6 of 13 (mostly second-generation) epigenetic clocks. Results provide preliminary evidence that volunteering might provide health benefits through slower biological aging and implicate health behaviors as one potential mechanism of such effects.