Integrating Structured-light 3D Depth Sensing and Voice Analysis as a Multimodal AI Approach for Early Screening of Acromegaly: A Narrative Review and Future Perspectives

Integrating Structured-light 3D Depth Sensing and Voice Analysis as a Multimodal AI Approach for Early Screening of Acromegaly: A Narrative Review and Future Perspectives | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 August 2025 V1 Latest version Share on Integrating Structured-light 3D Depth Sensing and Voice Analysis as a Multimodal AI Approach for Early Screening of Acromegaly: A Narrative Review and Future Perspectives Author : M 0009-0004-8362-9249 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175624409.99053689/v1 162 views 135 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background. Acromegaly is a rare endocrine disorder characterized by the chronic hypersecretion of growth hormone (GH) and elevated insulin-like growth factor 1 (IGF-1), often diagnosed late due to the subtle early signs. While biochemical assays and pituitary MRI are accurate, they are unsuitable for population-wide screening. Structured-light (SL) 3D depth sensing and automated voice analysis can detect early craniofacial and phonatory changes. Objective. To evaluate the potential of a multimodal AI framework integrating SL and voice biomarkers for earlier, scalable acromegaly detection. Methods. A narrative review (2010-2025) of PubMed, Scopus, and IEEE Xplore identified peerreviewed studies on 2D/3D facial analysis, SL depth sensing, voice biomarkers, and multimodal AI. Diagnostic accuracy, technical performance, and methodological quality were synthesized using the QUADAS-2 framework. Results. SL achieved sub-millimeter accuracy (RMSE 0.78-0.92 mm), with sensitivity ranging from 85% to 92% and specificity ranging from 88% to 94%. Voice analysis detected a lowered fundamental frequency, increased jitter/shimmer, and a reduced harmonics-to-noise ratio, with an AUC of 0.78-0.86. In one multicenter study (n = 524), the AUC of 0.84 exceeded the endocrinologists' 0.69. No study has tested both modalities in the same cohort. Discussion. SL captures stable morphological features, while voice analysis reflects earlier functional changes; integration could enhance the early detection of these changes. Translation to practice requires diverse, representative datasets, standardized acquisition protocols, and hardware-agnostic compatibility. Longitudinal monitoring could boost sensitivity and reduce false negatives. Economic modeling supports the cost-effectiveness of a tiered pathway. Conclusion. If validated in large, multicenter trials, this approach may reduce diagnostic delays in acromegaly and serve as a blueprint for multimodal AI in other rare, high-impact diseases. Supplementary Material File (ai for early screening of acromegaly.pdf) Download 421.69 KB Information & Authors Information Version history V1 Version 1 26 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords acromegaly artificial intelligence early screening multimodal diagnostics structured-light voice biomarkers Authors Affiliations M 0009-0004-8362-9249 [email protected] Institute of Endocrine and Metabolic Sciences, Vita-Salute San Raffaele University, IRCCS Ospedale San Raffaele View all articles by this author Metrics & Citations Metrics Article Usage 162 views 135 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation M. Integrating Structured-light 3D Depth Sensing and Voice Analysis as a Multimodal AI Approach for Early Screening of Acromegaly: A Narrative Review and Future Perspectives. Authorea . 26 August 2025. DOI: https://doi.org/10.22541/au.175624409.99053689/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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