Publications by authors named "Konstantina Vouzouneraki"
Article Synopsis
- There is a significant delay in diagnosing acromegaly, leading to worse health outcomes, and voice changes are commonly observed in affected individuals due to changes in orofacial and laryngeal structures.
- The study aimed to investigate the effectiveness of digital voice analysis combined with machine learning as a method to identify acromegaly by analyzing voice recordings taken from patients and controls.
- Results showed that the machine learning model was able to accurately detect acromegaly with an AUC of 0.84, outperforming experienced endocrinologists, and patients with acromegaly reported more voice-related issues compared to controls.
Objective: Carpal tunnel syndrome (CTS) is common in patients with acromegaly, with a reported prevalence of 19-64%. We studied CTS in a large national cohort of patients with acromegaly and the temporal relationship between the two diagnoses.
Design: Retrospective, nationwide, cohort study including patients diagnosed with acromegaly in Sweden, 2005-2017, identified in the Swedish Healthcare Registries.
Purpose: Patients with acromegaly have an increased risk of sleep apnea, but reported prevalence rates vary largely. Here we aimed to evaluate the sleep apnea prevalence in a large national cohort of patients with acromegaly, to examine possible risk factors, and to assess the proportion of patients diagnosed with sleep apnea prior to acromegaly diagnosis.
Methods: Cross-sectional multicenter study of 259 Swedish patients with acromegaly.