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Background/aim: Acromegaly is a rare disease caused by a pituitary tumor secreting excess growth hormone, which leads to acral growth, organ enlargement, and facial changes. Patients with acromegaly have an increased risk of type 2 diabetes, cardiovascular disease, and arthropathy. Due to the rarity and slow progression of the disease, there is a considerable diagnostic delay (5–8 years), which contributes to increased morbidity and mortality. This thesis is based on four studies aimed at investigating the presentation of sleep apnea and carpal tunnel syndrome (CTS) in patients with acromegaly and the potential for digital analysis of voice and face to identify patients with acromegaly. 

Methods and results: Paper I was a cross-sectional, multicenter study of 259 patients with acromegaly: 29% of the patients were previously diagnosed with sleep apnea, with more than half (57%) of these diagnosed prior to the diagnosis of acromegaly. Another 8% of this cohort were found to have undiagnosed sleep apnea by targeted clinical assessment and sleep investigation. Paper II was a retrospective, national registry-based study of 556 patients with a diagnosis of acromegaly from the National Patient Registry. It found a 6-fold higher incidence of CTS diagnosis and surgery prior to acromegaly diagnosis compared to the general population. The risk of CTS was higher in women with acromegaly, and 84% of patients with CTS were diagnosed and surgically treated before the diagnosis of acromegaly. The potential window of opportunity to diagnose acromegaly earlier led us to investigate new non-invasive screening tools. In Paper III, a multicenter cohort study, we collected voice recordings from 151 patients with acromegaly (23% biochemically active) and 139 matched controls to create a machine learning algorithm, which identified the voice of patients with acromegaly at higher accuracy than experienced endocrinologists (ROC AUC 0.84 vs 0.69). Both biochemically active and controlled patients with acromegaly reported increased voice impairment (Voice Handicap Index) compared to controls. In Paper IV, we used facial images from 155 patients and 153 controls from the same cohort and machine learning algorithms for face analysis to train several machine learning models. The best model matched the accuracy of the compound assessment of 12 experienced endocrinologists (ROC AUC 0.85 vs 0.89) in acromegaly prediction while showing a higher sensitivity (0.82 vs 0.66). 

Conclusions: The diagnostic delay in acromegaly leads to the presence of comorbidities long before the disease is recognized. In this time window, non-invasive screening tools based on facial and voice analysis may improve the chances for earlier diagnosis.