University of California, San Francisco
The Validation of the Diabetes Deep Neural Network Score (DNN score) for Screening for Type 2 Diabetes Mellitus (diabetes) is a single center, unblinded, observational study to clinically validating a previously developed remote digital biomarker, identified as the DNN score, to screen for diabetes. The previously developed DNN score provides a promising avenue to detect diabetes in these high-risk communities by leveraging photoplethysmography (PPG) technology on the commercial smartphone camera that is highly accessible. Our primary aim is to prospectively clinically validate the PPG DNN algorithm against the reference standards of glycated hemoglobin (HbA1c) for the presence of prevalent diabetes. Our vision is that this clinical trial may ultimately support an application to the Food and Drug Administration so that it can be incorporated into guideline-based screening.
Diabetes
Application Validation
NA
| Study Type : | INTERVENTIONAL |
| Estimated Enrollment : | 0 participants |
| Masking : | NONE |
| Primary Purpose : | DIAGNOSTIC |
| Official Title : | Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening |
| Actual Study Start Date : | 2023-06-01 |
| Estimated Primary Completion Date : | 2023-06-01 |
| Estimated Study Completion Date : | 2025-04-01 |
Information not available for Arms and Intervention/treatment
| Ages Eligible for Study: | 18 Years |
| Sexes Eligible for Study: | ALL |
| Accepts Healthy Volunteers: | 1 |
Want to participate in this study, select a site at your convenience, send yourself email to get contact details and prescreening steps.
Not yet recruiting
University of California, San Francisco
San Francisco, California, United States, 94143