Digital voice analysis as a biomarker of acromegaly

Context: There is a considerable diagnostic delay in acromegaly contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly.

Objective: Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning.

Methods: 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.

Results: 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%).

Conclusion: 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.