ABIR ELBEJI, MÉGANE PIZZIMENTI, GLORIA A. AGUAYO, AURELIE FISCHER, HANIN AYADI, FRANCK MAUVAIS-JARVIS, JEAN-PIERRE RIVELINE, VLADIMIR DESPOTOVIC, GUY FAGHERAZZI
{"title":"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","authors":"ABIR ELBEJI, MÉGANE PIZZIMENTI, GLORIA A. AGUAYO, AURELIE FISCHER, HANIN AYADI, FRANCK MAUVAIS-JARVIS, JEAN-PIERRE RIVELINE, VLADIMIR DESPOTOVIC, GUY FAGHERAZZI","doi":"10.2337/db24-1308-p","DOIUrl":null,"url":null,"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. 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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. 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1308-P: A Voice-Based AI Algorithm Can Predict Type 2 Diabetes Status—Findings from the Colive Voice Study on U.S. Adult Participants
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.
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.