1308-P: A Voice-Based AI Algorithm Can Predict Type 2 Diabetes Status—Findings from the Colive Voice Study on U.S. Adult Participants

IF 6.2 1区医学 Q1 ENDOCRINOLOGY & METABOLISM

Diabetes Pub Date : 2024-07-19 DOI:10.2337/db24-1308-p

ABIR ELBEJI, MÉGANE PIZZIMENTI, GLORIA A. AGUAYO, AURELIE FISCHER, HANIN AYADI, FRANCK MAUVAIS-JARVIS, JEAN-PIERRE RIVELINE, VLADIMIR DESPOTOVIC, GUY FAGHERAZZI

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引用次数: 0

Abstract

Introduction: Reducing undiagnosed type 2 diabetes (T2D) cases worldwide is an urgent public health challenge. Most current screening methods are invasive, lab-based, and costly. Meanwhile, there is a growing focus on noninvasive T2D detection through advanced artificial intelligence (AI) and digital technology. This study explores the feasibility of using a voice-based AI algorithm to predict T2D status in adults, a preliminary step toward innovative screening tools. Objective: To develop and assess the performance of a voice-based AI algorithm for T2D status detection in the adult population in the US. Methods: We analyzed text reading voice recordings from 607 US participants from the Colive Voice study, adhering to the CONSORT AI standards. We trained and cross-validated algorithms with BYOL-S/CvT embeddings for each gender, evaluating them on accuracy, precision, recall, and AUC. Performance of the best models was stratified by age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using a Bland-Altman analysis. Results: We analyzed 323 females and 284 males; Females with T2D (age: 49.5 years, BMI: 35.8 kg/m²) vs without (40.0 years, 28.0 kg/m²). Males with T2D (47.6 years, 32.8 kg/m²) vs without (41.6 years, 26.6 kg/m²). The voice-based algorithm achieved good overall predictive capacity (AUC=75% for males, 71% for females) and correctly predicted 71% of male and 66% of female T2D cases. It is enhanced in females aged 60 years (AUC=74%) or older but also with the presence of hypertension for both genders (AUC=75%). We observed an overall agreement above 93% with the ADA risk score. Conclusion: This study demonstrates the feasibility of detecting T2D using exclusively voice features. It is the first step toward using voice analysis as a first-line T2D screening strategy. While the findings are promising, further research and validation are necessary to specifically target early-stage T2D cases. Disclosure A. Elbeji: None. M. Pizzimenti: None. G.A. Aguayo: None. A. Fischer: None. H. Ayadi: None. F. Mauvais-Jarvis: None. J. Riveline: Board Member; Abbott, Novo Nordisk A/S, Sanofi, Eli Lilly and Company, Medtronic, Dexcom, Inc., Insulet Corporation, Air Liquide, AstraZeneca. V. Despotovic: None. G. Fagherazzi: Speaker's Bureau; Sanofi. Advisory Panel; Timkl, SAB Biotherapeutics, Inc., Vitalaire, Roche Diabetes Care.

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1308-P：基于语音的人工智能算法可预测 2 型糖尿病状态--对美国成人参与者进行的 Colive Voice 研究结果

导言：减少全球未确诊的 2 型糖尿病（T2D）病例是一项紧迫的公共卫生挑战。目前大多数筛查方法都是侵入性的、基于实验室的，而且成本高昂。与此同时，人们越来越关注通过先进的人工智能（AI）和数字技术进行无创 T2D 检测。本研究探讨了使用基于语音的人工智能算法预测成人 T2D 状态的可行性，这是迈向创新筛查工具的第一步。研究目的开发并评估基于语音的人工智能算法在美国成年人群中检测 T2D 状态的性能。方法我们分析了来自 Colive Voice 研究的 607 名美国参与者的文本阅读语音记录，并遵守 CONSORT AI 标准。我们使用 BYOL-S/CvT 嵌入对不同性别的算法进行了训练和交叉验证，并根据准确度、精确度、召回率和 AUC 对其进行了评估。根据年龄、体重指数和高血压对最佳模型的性能进行了分层，并使用布兰-阿尔特曼分析将其与美国糖尿病协会（ADA）的 T2D 风险评估评分进行了比较。结果：我们分析了 323 名女性和 284 名男性；患有 T2D 的女性（年龄：49.5 岁，体重指数：35.8 kg/m²）与未患 T2D 的女性（年龄：40.0 岁，体重指数：28.0 kg/m²）。患有 T2D 的男性（年龄：47.6 岁，体重指数：32.8 kg/m²）与未患有 T2D 的男性（年龄：41.6 岁，体重指数：26.6 kg/m²）。基于语音的算法具有良好的总体预测能力（AUC=男性75%，女性71%），能正确预测71%的男性和66%的女性T2D病例。对于 60 岁（AUC=74%）或以上的女性，该算法的预测能力更强，但对于患有高血压的男性和女性（AUC=75%），该算法的预测能力也更强。我们观察到与 ADA 风险评分的总体一致性超过 93%。结论：这项研究证明了完全使用语音特征检测 T2D 的可行性。这是将语音分析用作 T2D 一线筛查策略的第一步。虽然研究结果很有希望，但仍需进一步研究和验证，以专门针对早期 T2D 病例。披露 A. Elbeji：无。M. Pizzimenti：无。G.A. Aguayo：无。A. Fischer：无。H. Ayadi：无。F. Mauvais-Jarvis：F. Mauvais-Jarvis: None.J. Riveline：董事会成员；雅培、诺和诺德公司、赛诺菲、礼来公司、美敦力、Dexcom 公司、Insulet 公司、液化空气公司、阿斯利康公司。V. Despotovic：无。G. Fagherazzi: Speaker's Bureau; Sanofi.顾问团；Timkl、SAB Biotherapeutics、Inc.、Vitalaire、Roche Diabetes Care。

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来源期刊

Diabetes 医学-内分泌学与代谢

CiteScore

12.50

自引率

2.60%

发文量

1968

审稿时长

1 months

期刊介绍： Diabetes is a scientific journal that publishes original research exploring the physiological and pathophysiological aspects of diabetes mellitus. We encourage submissions of manuscripts pertaining to laboratory, animal, or human research, covering a wide range of topics. Our primary focus is on investigative reports investigating various aspects such as the development and progression of diabetes, along with its associated complications. We also welcome studies delving into normal and pathological pancreatic islet function and intermediary metabolism, as well as exploring the mechanisms of drug and hormone action from a pharmacological perspective. Additionally, we encourage submissions that delve into the biochemical and molecular aspects of both normal and abnormal biological processes. However, it is important to note that we do not publish studies relating to diabetes education or the application of accepted therapeutic and diagnostic approaches to patients with diabetes mellitus. Our aim is to provide a platform for research that contributes to advancing our understanding of the underlying mechanisms and processes of diabetes.