A digital biomarker of diabetes from smartphone-based vascular signals
The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045 1 . The insidious onset of type 2 diabetes delays diagnosis and increases morbidity 2 . Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethy...
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| Published in | Nature medicine Vol. 26; no. 10; pp. 1576 - 1582 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.10.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1078-8956 1546-170X 1546-170X |
| DOI | 10.1038/s41591-020-1010-5 |
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| Abstract | The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045
1
. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity
2
. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the ‘primary cohort’), which we then validated in a separate cohort of 7,806 individuals (the ‘contemporary cohort’) and a cohort of 181 prospectively enrolled individuals from three clinics (the ‘clinic cohort’). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (
P
≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.
A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis. |
|---|---|
| AbstractList | The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045
. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity
. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045.sup.1. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity.sup.2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P [less than or equal to] 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography (PPG) could provide a widely-accessible digital biomarker for diabetes. Here, we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based PPG from an initial cohort of 53,870 individuals (the “Primary Cohort”), which was then validated in a separate cohort of 7,806 individuals (the “Contemporary Cohort”), and a cohort of 181 prospectively-enrolled individuals from three clinics (the “Clinic Cohort”). The DNN achieved an area under the curve (AUC) for prevalent diabetes of 0.766 in the Primary Cohort (95% confidence interval (CI): 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the Contemporary Cohort (95% CI: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN Score, was included in a regression analysis alongside age, gender, race/ethnicity, and body mass index, the AUC was 0.830 and the DNN Score remained independently predictive of diabetes. The performance of the DNN in the Clinic Cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN Score and hemoglobin A1c (HbA1c) (p≤0.001) among those with HbA1c. These findings demonstrate that smartphone-based PPG provides a readily attainable, noninvasive digital biomarker of prevalent diabetes. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045.sup.1. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity.sup.2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P [less than or equal to] 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the ‘primary cohort’), which we then validated in a separate cohort of 7,806 individuals (the ‘contemporary cohort’) and a cohort of 181 prospectively enrolled individuals from three clinics (the ‘clinic cohort’). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis. The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045 1 . The insidious onset of type 2 diabetes delays diagnosis and increases morbidity 2 . Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the ‘primary cohort’), which we then validated in a separate cohort of 7,806 individuals (the ‘contemporary cohort’) and a cohort of 181 prospectively enrolled individuals from three clinics (the ‘clinic cohort’). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c ( P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis. |
| Audience | Academic |
| Author | Tison, Geoffrey H. Marcus, Gregory M. Hughes, J. Weston Kuhar, Peter Avram, Robert Olgin, Jeffrey E. Pletcher, Mark J. Aschbacher, Kirstin |
| AuthorAffiliation | 3 Department of Computer Science, University of California, Berkeley (Berkeley, CA, United States), 253 Cory Hall, Berkeley, California, 94720-1770, United States of America 2 Azumio, inc (Palo Alto, CA, United States), 145, 255 Shoreline Drive, Redwood City, California, 94065, United States of America 4 Department of Epidemiology and Biostatistics, University of California San Francisco (San Francisco, CA, United States), 550 16 th Ave, Mission Hall 2 nd Floor, San Francisco, California, 94143-0560, United States of America 5 Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA 1 Division of Cardiology and Cardiovascular Research Institute, Department of Medicine, University of California, San Francisco, Cardiology (San Francisco, CA, United States), 505 Parnassus Avenue, San Francisco, California, 94143, United States of America 6 Department of Psychiatry, Weill Institute for Neurosciences, University of California San Fran |
| AuthorAffiliation_xml | – name: 6 Department of Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, California, USA – name: 2 Azumio, inc (Palo Alto, CA, United States), 145, 255 Shoreline Drive, Redwood City, California, 94065, United States of America – name: 4 Department of Epidemiology and Biostatistics, University of California San Francisco (San Francisco, CA, United States), 550 16 th Ave, Mission Hall 2 nd Floor, San Francisco, California, 94143-0560, United States of America – name: 5 Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA – name: 3 Department of Computer Science, University of California, Berkeley (Berkeley, CA, United States), 253 Cory Hall, Berkeley, California, 94720-1770, United States of America – name: 1 Division of Cardiology and Cardiovascular Research Institute, Department of Medicine, University of California, San Francisco, Cardiology (San Francisco, CA, United States), 505 Parnassus Avenue, San Francisco, California, 94143, United States of America |
| Author_xml | – sequence: 1 givenname: Robert orcidid: 0000-0002-8490-0270 surname: Avram fullname: Avram, Robert organization: Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco – sequence: 2 givenname: Jeffrey E. surname: Olgin fullname: Olgin, Jeffrey E. organization: Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco – sequence: 3 givenname: Peter surname: Kuhar fullname: Kuhar, Peter organization: Azumio, Inc – sequence: 4 givenname: J. Weston surname: Hughes fullname: Hughes, J. Weston organization: Department of Computer Science, University of California, Berkeley – sequence: 5 givenname: Gregory M. surname: Marcus fullname: Marcus, Gregory M. organization: Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco – sequence: 6 givenname: Mark J. surname: Pletcher fullname: Pletcher, Mark J. organization: Department of Epidemiology and Biostatistics, University of California, San Francisco – sequence: 7 givenname: Kirstin orcidid: 0000-0001-6191-6432 surname: Aschbacher fullname: Aschbacher, Kirstin organization: Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, Bakar Computational Health Sciences Institute, University of California, San Francisco, Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco – sequence: 8 givenname: Geoffrey H. orcidid: 0000-0002-0310-3326 surname: Tison fullname: Tison, Geoffrey H. email: geoff.tison@ucsf.edu organization: Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, Bakar Computational Health Sciences Institute, University of California, San Francisco |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32807931$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3132/dvdr.2008.047 10.1001/jama.2016.17216 10.1016/j.hrthm.2015.08.004 10.1109/13.804537 10.1038/s41591-018-0322-1 10.1109/TBME.2016.2554661 10.1109/TBME.2007.897805 10.1056/NEJMoa052911 10.1016/S0002-9149(00)00920-6 10.1253/circj.70.304 10.1038/s41746-019-0090-4 10.1038/nature14539 10.1016/j.diabres.2018.02.023 10.4158/EP161267.OR 10.1016/j.bbe.2018.09.007 10.1016/j.patrec.2005.10.010 10.1038/s41746-019-0134-9 10.1088/0967-3334/35/10/2027 10.1371/journal.pone.0195166 10.1038/s41746-019-0136-7 10.2174/157340312801215782 10.1136/heartjnl-2015-309119 10.1161/01.CIR.0000066324.74807.95 10.1136/bmj.d7163 10.2337/dc18-S002 10.2337/dc10-1235 10.1371/journal.pone.0024946 10.1016/j.patrec.2008.08.010 10.2337/diacare.25.3.471 10.1016/j.jacc.2019.08.1055 10.2337/diacare.26.3.725 10.1016/S0895-4356(03)00177-X 10.2337/diacare.15.7.815 10.1088/0967-3334/28/3/R01 10.1001/jamainternmed.2017.7821 10.1109/ICCV.2015.123 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2020 COPYRIGHT 2020 Nature Publishing Group The Author(s), under exclusive licence to Springer Nature America, Inc. 2020. |
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| DOI | 10.1038/s41591-020-1010-5 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 These authors contributed equally as co-senior author: K.A. G.H.T. Author Contributions: J.E.O., R.A., G.H.T., and K.A. contributed to the study design. P.K., J.E.O., R.A., K.A., and G.H.T. contributed to data collection. R.A. and G.H.T. performed data cleaning and analysis, ran experiments and created tables and figures. R.A., J.E.O., P.K., J.W.H., G.M.M., M.J.P., K.A., and G.H.T. contributed to data interpretation and writing. G.H.T., J.E.O., and K.A. supervised. G.H.T. and K.A. contributed equally as co-senior authors. All authors read and approved the submitted manuscript. |
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| References | Bertoni, Krop, Anderson, Brancati (CR3) 2002; 25 Noble, Mathur, Dent, Meads, Greenhalgh (CR23) 2011; 343 Elgendi (CR5) 2019; 2 Harris, Klein, Welborn, Knuiman (CR2) 1992; 15 Lindström, Tuomilehto (CR33) 2003; 26 Avram (CR11) 2019; 2 Pilt, Meigas, Ferenets, Temitski, Viigimaa (CR15) 2014; 35 Camacho, Shah, Schrader, Wong, Burge (CR27) 2016; 22 Schönauer (CR16) 2008; 5 LeCun, Bengio, Hinton (CR17) 2015; 521 Hannun (CR20) 2019; 25 Carnethon, Golden, Folsom, Haskell, Liao (CR12) 2003; 107 CR38 CR37 CR14 Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (CR39) 2014; 15 CR31 Karakaya, Akin, Karagaoglu, Gurlek (CR28) 2014; 19 Alty, Angarita-Jaimes, Millasseau, Chowienczyk (CR6) 2007; 54 Guo (CR13) 2019; 74 Nirala, Periyasamy, Singh, Kumar (CR25) 2019; 39 (CR34) 2018; 41 Elgendi (CR35) 2012; 8 Dixit (CR21) 2016; 13 Emami (CR36) 1999; 42 Pisano (CR29) 2005; 353 Allen (CR4) 2007; 28 CR8 Fawcett (CR22) 2006; 27 Singh (CR10) 2000; 86 Gulshan (CR18) 2016; 316 Coravos, Khozin, Mandl (CR9) 2019; 2 Selvin, Steffes, Gregg, Brancati, Coresh (CR26) 2010; 34 Mathews, Agmas, Cachay (CR30) 2011; 6 Kim (CR32) 2016; 102 CR43 CR42 Zhang (CR19) 2018; 178 CR41 CR40 Otsuka, Kawada, Katsumata, Ibuki (CR7) 2006; 70 Benichou (CR46) 2018; 13 Cho (CR1) 2018; 138 Moreno (CR24) 2017; 64 Ferri, Hernández-Orallo, Modroiu (CR44) 2009; 30 Glas, Lijmer, Prins, Bonsel, Bossuyt (CR45) 2003; 56 JE Camacho (1010_CR27) 2016; 22 MI Harris (1010_CR2) 1992; 15 A Coravos (1010_CR9) 2019; 2 K Pilt (1010_CR15) 2014; 35 D Noble (1010_CR23) 2011; 343 1010_CR31 N Nirala (1010_CR25) 2019; 39 M Elgendi (1010_CR5) 2019; 2 SR Alty (1010_CR6) 2007; 54 E Selvin (1010_CR26) 2010; 34 ED Pisano (1010_CR29) 2005; 353 WC Mathews (1010_CR30) 2011; 6 J Lindström (1010_CR33) 2003; 26 1010_CR37 JP Singh (1010_CR10) 2000; 86 Y Guo (1010_CR13) 2019; 74 1010_CR38 C Ferri (1010_CR44) 2009; 30 American Diabetes Association (1010_CR34) 2018; 41 1010_CR14 V Gulshan (1010_CR18) 2016; 316 AS Glas (1010_CR45) 2003; 56 T Otsuka (1010_CR7) 2006; 70 Y LeCun (1010_CR17) 2015; 521 1010_CR8 H Zhang (1010_CR19) 2018; 178 D-I Kim (1010_CR32) 2016; 102 S Dixit (1010_CR21) 2016; 13 AG Bertoni (1010_CR3) 2002; 25 EM Moreno (1010_CR24) 2017; 64 NH Cho (1010_CR1) 2018; 138 MR Carnethon (1010_CR12) 2003; 107 N Srivastava (1010_CR39) 2014; 15 1010_CR42 1010_CR43 1010_CR40 1010_CR41 T Benichou (1010_CR46) 2018; 13 R Avram (1010_CR11) 2019; 2 M Schönauer (1010_CR16) 2008; 5 M Elgendi (1010_CR35) 2012; 8 AY Hannun (1010_CR20) 2019; 25 J Allen (1010_CR4) 2007; 28 J Karakaya (1010_CR28) 2014; 19 S Emami (1010_CR36) 1999; 42 T Fawcett (1010_CR22) 2006; 27 33060831 - Nat Rev Endocrinol. 2020 Dec;16(12):681-682. doi: 10.1038/s41574-020-00433-6 |
| References_xml | – volume: 5 start-page: 336 year: 2008 end-page: 344 ident: CR16 article-title: Cardiac autonomic diabetic neuropathy publication-title: Diabetes Vasc. Dis. Res. doi: 10.3132/dvdr.2008.047 – volume: 316 start-page: 2402 year: 2016 end-page: 2410 ident: CR18 article-title: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs publication-title: J. Am. Med. Assoc. doi: 10.1001/jama.2016.17216 – volume: 13 start-page: 3 year: 2016 end-page: 9 ident: CR21 article-title: Secondhand smoke and atrial fibrillation: data from the Health eHeart Study publication-title: Heart Rhythm doi: 10.1016/j.hrthm.2015.08.004 – ident: CR43 – volume: 42 start-page: 311 year: 1999 end-page: 314 ident: CR36 article-title: New methods for computing interpolation and decimation using polyphase decomposition publication-title: IEEE Trans. Educ. doi: 10.1109/13.804537 – volume: 25 start-page: 1 year: 2019 end-page: 11 ident: CR20 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nat. Med. doi: 10.1038/s41591-018-0322-1 – ident: CR14 – ident: CR37 – volume: 64 start-page: 341 year: 2017 end-page: 351 ident: CR24 article-title: Type 2 diabetes screening test by means of a pulse oximeter publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2016.2554661 – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: CR39 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 54 start-page: 2268 year: 2007 end-page: 2275 ident: CR6 article-title: Predicting arterial stiffness from the digital volume pulse waveform publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2007.897805 – volume: 353 start-page: 1773 year: 2005 end-page: 1783 ident: CR29 article-title: Diagnostic performance of digital versus film mammography for breast-cancer screening publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa052911 – volume: 86 start-page: 309 year: 2000 end-page: 312 ident: CR10 article-title: Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study) publication-title: Am. J. Cardiol. doi: 10.1016/S0002-9149(00)00920-6 – ident: CR8 – volume: 70 start-page: 304 year: 2006 end-page: 310 ident: CR7 article-title: Utility of second derivative of the finger photoplethysmogram for the estimation of the risk of coronary heart disease in the general population publication-title: Circ. J. doi: 10.1253/circj.70.304 – volume: 2 year: 2019 ident: CR9 article-title: Developing and adopting safe and effective digital biomarkers to improve patient outcomes publication-title: npj Digit. Med. doi: 10.1038/s41746-019-0090-4 – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: CR17 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 138 start-page: 271 year: 2018 end-page: 281 ident: CR1 article-title: IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045 publication-title: Diabetes Res. Clin. Pract. doi: 10.1016/j.diabres.2018.02.023 – ident: CR40 – volume: 22 start-page: 1288 year: 2016 end-page: 1295 ident: CR27 article-title: Performance of A1C versus OGTT for the diagnosis of prediabetes in a community-based screening publication-title: Endocr. Pract. doi: 10.4158/EP161267.OR – volume: 39 start-page: 38 year: 2019 end-page: 51 ident: CR25 article-title: Detection of type-2 diabetes using characteristics of toe photoplethysmogram by applying support vector machine publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2018.09.007 – ident: CR42 – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: CR22 article-title: An introduction to ROC analysis publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 2 year: 2019 ident: CR11 article-title: Real-world heart rate norms in the Health eHeart study publication-title: npj Dig. Med. doi: 10.1038/s41746-019-0134-9 – volume: 35 start-page: 2027 year: 2014 end-page: 2036 ident: CR15 article-title: Photoplethysmographic signal waveform index for detection of increased arterial stiffness publication-title: Physiol. Meas. doi: 10.1088/0967-3334/35/10/2027 – volume: 13 start-page: e0195166 year: 2018 ident: CR46 article-title: Heart rate variability in type 2 diabetes mellitus: a systematic review and meta-analysis publication-title: PLoS ONE doi: 10.1371/journal.pone.0195166 – volume: 2 year: 2019 ident: CR5 article-title: The use of photoplethysmography for assessing hypertension publication-title: npj Digit. Med. doi: 10.1038/s41746-019-0136-7 – volume: 8 start-page: 14 year: 2012 end-page: 25 ident: CR35 article-title: On the analysis of fingertip photoplethysmogram signals publication-title: Curr. Cardiol. Rev. doi: 10.2174/157340312801215782 – volume: 102 start-page: 1757 year: 2016 end-page: 1762 ident: CR32 article-title: The association between resting heart rate and type 2 diabetes and hypertension in Korean adults publication-title: Heart doi: 10.1136/heartjnl-2015-309119 – volume: 107 start-page: 2190 year: 2003 end-page: 2195 ident: CR12 article-title: Prospective investigation of autonomic nervous system function and the development of type 2 diabetes publication-title: Circulation doi: 10.1161/01.CIR.0000066324.74807.95 – volume: 343 start-page: d7163 year: 2011 end-page: d7163 ident: CR23 article-title: Risk models and scores for type 2 diabetes: systematic review publication-title: Br. Med. J. doi: 10.1136/bmj.d7163 – volume: 41 start-page: S13 year: 2018 end-page: S27 ident: CR34 article-title: Classification and diagnosis of diabetes: standards of medical care in diabetes—2018 publication-title: Diabetes Care doi: 10.2337/dc18-S002 – ident: CR38 – volume: 19 start-page: 1051 year: 2014 end-page: 1057 ident: CR28 article-title: The performance of hemoglobin A1c against fasting plasma glucose and oral glucose tolerance test in detecting prediabetes and diabetes publication-title: J. Res. Med. Sci. – volume: 34 start-page: 84 year: 2010 end-page: 89 ident: CR26 article-title: Performance of A1C for the classification and prediction of diabetes publication-title: Diabetes Care doi: 10.2337/dc10-1235 – volume: 6 start-page: e24946 year: 2011 ident: CR30 article-title: Comparative accuracy of anal and cervical cytology in screening for moderate to severe dysplasia by magnification guided punch biopsy: a meta-analysis publication-title: PLoS ONE doi: 10.1371/journal.pone.0024946 – ident: CR31 – volume: 30 start-page: 27 year: 2009 end-page: 38 ident: CR44 article-title: An experimental comparison of performance measures for classification publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.08.010 – volume: 25 start-page: 471 year: 2002 end-page: 475 ident: CR3 article-title: Diabetes-related morbidity and mortality in a national sample of U.S. elders publication-title: Diabetes Care doi: 10.2337/diacare.25.3.471 – volume: 74 start-page: 2162 year: 2019 end-page: 2174 ident: CR13 article-title: Genome-wide assessment for resting heart rate and shared genetics with cardiometabolic traits and type 2 diabetes publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2019.08.1055 – volume: 26 start-page: 725 year: 2003 end-page: 731 ident: CR33 article-title: The diabetes risk score: a practical tool to predict type 2 diabetes risk publication-title: Diabetes Care doi: 10.2337/diacare.26.3.725 – volume: 56 start-page: 1129 year: 2003 end-page: 1135 ident: CR45 article-title: The diagnostic odds ratio: a single indicator of test performance publication-title: J. Clin. Epidemiol. doi: 10.1016/S0895-4356(03)00177-X – volume: 15 start-page: 815 year: 1992 end-page: 819 ident: CR2 article-title: Onset of NIDDM occurs at least 4–7 yr before clinical diagnosis publication-title: Diabetes Care doi: 10.2337/diacare.15.7.815 – volume: 28 start-page: R1 year: 2007 end-page: R39 ident: CR4 article-title: Photoplethysmography and its application in clinical physiological measurement publication-title: Physiol. Meas. doi: 10.1088/0967-3334/28/3/R01 – ident: CR41 – volume: 178 start-page: 239 year: 2018 end-page: 247 ident: CR19 article-title: Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China publication-title: JAMA Intern. Med. doi: 10.1001/jamainternmed.2017.7821 – volume: 6 start-page: e24946 year: 2011 ident: 1010_CR30 publication-title: PLoS ONE doi: 10.1371/journal.pone.0024946 – volume: 102 start-page: 1757 year: 2016 ident: 1010_CR32 publication-title: Heart doi: 10.1136/heartjnl-2015-309119 – volume: 15 start-page: 1929 year: 2014 ident: 1010_CR39 publication-title: J. Mach. Learn. Res. – volume: 521 start-page: 436 year: 2015 ident: 1010_CR17 publication-title: Nature doi: 10.1038/nature14539 – volume: 70 start-page: 304 year: 2006 ident: 1010_CR7 publication-title: Circ. J. doi: 10.1253/circj.70.304 – volume: 107 start-page: 2190 year: 2003 ident: 1010_CR12 publication-title: Circulation doi: 10.1161/01.CIR.0000066324.74807.95 – ident: 1010_CR8 – volume: 42 start-page: 311 year: 1999 ident: 1010_CR36 publication-title: IEEE Trans. Educ. doi: 10.1109/13.804537 – ident: 1010_CR38 – volume: 35 start-page: 2027 year: 2014 ident: 1010_CR15 publication-title: Physiol. Meas. doi: 10.1088/0967-3334/35/10/2027 – volume: 26 start-page: 725 year: 2003 ident: 1010_CR33 publication-title: Diabetes Care doi: 10.2337/diacare.26.3.725 – ident: 1010_CR40 – volume: 27 start-page: 861 year: 2006 ident: 1010_CR22 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 343 start-page: d7163 year: 2011 ident: 1010_CR23 publication-title: Br. Med. J. doi: 10.1136/bmj.d7163 – ident: 1010_CR42 – volume: 30 start-page: 27 year: 2009 ident: 1010_CR44 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.08.010 – volume: 25 start-page: 471 year: 2002 ident: 1010_CR3 publication-title: Diabetes Care doi: 10.2337/diacare.25.3.471 – volume: 316 start-page: 2402 year: 2016 ident: 1010_CR18 publication-title: J. Am. Med. Assoc. doi: 10.1001/jama.2016.17216 – volume: 138 start-page: 271 year: 2018 ident: 1010_CR1 publication-title: Diabetes Res. Clin. Pract. doi: 10.1016/j.diabres.2018.02.023 – volume: 25 start-page: 1 year: 2019 ident: 1010_CR20 publication-title: Nat. Med. doi: 10.1038/s41591-018-0322-1 – volume: 15 start-page: 815 year: 1992 ident: 1010_CR2 publication-title: Diabetes Care doi: 10.2337/diacare.15.7.815 – volume: 74 start-page: 2162 year: 2019 ident: 1010_CR13 publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2019.08.1055 – volume: 13 start-page: 3 year: 2016 ident: 1010_CR21 publication-title: Heart Rhythm doi: 10.1016/j.hrthm.2015.08.004 – ident: 1010_CR31 – volume: 353 start-page: 1773 year: 2005 ident: 1010_CR29 publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa052911 – volume: 41 start-page: S13 year: 2018 ident: 1010_CR34 publication-title: Diabetes Care doi: 10.2337/dc18-S002 – volume: 86 start-page: 309 year: 2000 ident: 1010_CR10 publication-title: Am. J. 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| Snippet | The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045
1
. The insidious onset of type 2 diabetes delays... The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045 . The insidious onset of type 2 diabetes delays... The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045.sup.1. The insidious onset of type 2 diabetes... The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays... |
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| SubjectTerms | 692/53/2423 692/699/2743/137 Adult Aged Aged, 80 and over Algorithms Artificial neural networks Biological markers Biomarkers Biomarkers - analysis Biomedical and Life Sciences Biomedicine Body mass index Body size Cancer Research Cohort Studies Confidence intervals Datasets as Topic Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - epidemiology Diabetes Mellitus, Type 2 - physiopathology Diagnosis Female Health aspects Heart Rate - physiology Hemoglobin Humans Infectious Diseases Letter Male Metabolic Diseases Middle Aged Molecular Medicine Neural networks Neural Networks, Computer Neurosciences Performance prediction Photoplethysmography - instrumentation Photoplethysmography - methods Predictive Value of Tests Prevalence Regional Blood Flow - physiology Regression analysis Sensitivity Sensitivity and Specificity Signal Processing, Computer-Assisted - instrumentation Smart phones Smartphone Smartphones Statistical analysis Technology application Telemetry - instrumentation Telemetry - methods |
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| Title | A digital biomarker of diabetes from smartphone-based vascular signals |
| URI | https://link.springer.com/article/10.1038/s41591-020-1010-5 https://www.ncbi.nlm.nih.gov/pubmed/32807931 https://www.proquest.com/docview/2828066403 https://www.proquest.com/docview/2435190216 https://pubmed.ncbi.nlm.nih.gov/PMC8483886 https://www.ncbi.nlm.nih.gov/pmc/articles/8483886 |
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