NCT05303051

Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening

• Status: Withdrawn
• Phase: N/A
• Study type: Interventional
• Enrollment: 0 (actual)
• Sponsor: University of California, San Francisco · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

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.

Conditions

• Diabetes

Interventions

Type	Name	Description
DEVICE	Application Validation	After creating accounts, participants in both groups will download the Azumio Instant Diabetes Test and provide a Photoplethysmography (PPG) waveforms by placing their index finger over their smartphone camera for 20 seconds to provide PPG waveform data for the study .

Timeline

• Start date: 2023-06-01
• Primary completion: 2023-06-01
• Completion: 2025-04-01
• First posted: 2022-03-31
• Last updated: 2025-04-08

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study