Publications by authors named "Abraham Baez-Suarez"
Article Synopsis
- Loneliness and social isolation are linked to negative health effects like heart disease and may speed up biological aging, as shown by AI-enabled heart studies.
- The study analyzed over 280,000 adults to assess social isolation and its effects, finding that increased social connections led to a healthier biological age and reduced risk of death.
- The findings suggest that improving social connections is essential for health care, as social isolation can significantly impact aging and overall mortality rates.
Article Synopsis
- Researchers tested an AI-enhanced ECG for detecting cardiac amyloidosis (CA) in a group of 440 diagnosed patients, compared to a control group of 6,600 who were matched by age and sex.
- The AI's performance showed an area under the curve (AUC) of 0.84, which is a decrease from an earlier study that reported an AUC of 0.91, indicating slight deterioration in accuracy.
- The AI performed well across different racial groups but struggled with Hispanic patients and conditions like left ventricular hypertrophy and left bundle branch block, highlighting the need for targeted improvements in these areas.
Introduction: A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM.
View Article and Find Full Text PDFBackground: Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality.
View Article and Find Full Text PDFObjective: To study the relationship between the sex probability derived from the artificial intelligence (AI)-augmented electrocardiogram (ECG) and sex hormone levels.
Patients And Methods: Adult patients with total testosterone (TT; ng/dL) or estradiol (E2; pg/mL) levels (January 1, 2000, to December 31, 2020) with ECGs obtained within 6 months of the blood sample were identified. The closest ECG to the blood test was used.
Objective: To compare the artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA).
Background: MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA.