Digital voice analysis as a biomarker of acromegaly.

Abstract

There is a considerable diagnostic delay in acromegaly contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly. Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning. Voice recordings from patients with acromegaly and matched controls were collected using a mobile phone at Swedish university hospitals. Anthropometric and clinical data and the Voice Handicap Index (VHI) were assessed. Digital voice analysis of a sustained and stable vowel [a] resulted in 3274 parameters, which were used for training of machine learning models classifying the speaker as "acromegaly" or "control". The machine learning model was trained with 76% of the data and the remaining 24% was used to assess its performance. For comparison, voice recordings of 50 pairs of participants were assessed by 12 experienced endocrinologists. We included 151 Swedish patients with acromegaly (13% biochemically active and 10% newly diagnosed) and 139 matched controls. The machine learning model identified patients with acromegaly more accurately [area under the receiver operating curve (ROC AUC) 0.84] than experienced endocrinologists (ROC AUC 0.69). Self-reported voice problems were more pronounced in patients with acromegaly than matched controls (median VHI 6 vs 2, P < .01) with higher prevalence of clinically significant voice handicap (VHI ≥20: 22.5% vs 3.6%). Digital voice analysis can identify patients with acromegaly from short voice recordings with high accuracy. Patients with acromegaly experience more voice disorders than matched controls.