ArticleThe Journal of clinical endocrinology and metabolism2025
Digital Voice Analysis as a Biomarker of Acromegaly.
Article in The Journal of clinical endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Current controversies in acromegaly care.The Journal of clinical endocrinology and metabolism · 2026Review
- Acromegaly diagnosis.The Journal of clinical endocrinology and metabolism · 2026Review
- Detection of Acromegaly From Facial Images Using Machine Learning: A Comparison With Clinical Experts.Journal of the Endocrine Society · 2026Article
- Artificial intelligence in acromegaly: Why, when and how.Pituitary · 2025Article
- Digital Voice Analysis as a Biomarker of Acromegaly.The Journal of clinical endocrinology and metabolism · 2025Article
- An acoustic model of speech dysprosody in patients with Parkinson's disease.Frontiers in human neuroscience · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
contextThere is a considerable diagnostic delay in acromegaly, contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly.
objectiveOur aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning.
methodsVoice 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 models were trained with 76% of the data and the remaining 24% was used to assess their performance. For comparison, voice recordings of 50 pairs of participants were assessed by 12 experienced endocrinologists.
resultsWe 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%).
conclusionDigital voice analysis can identify patients with acromegaly from short voice recordings with high accuracy. Patients with acromegaly experience more voice disorders than matched controls.
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