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

NCT05303051 | Withdrawn FDA Device

• 1 other identifier: 21-35207
• Study Type: interventional
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (3)

• The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement, based an average of two PPG measurements.

  Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance by the the area under the receiver operating characteristic (AUROC) of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.

  PPG measurements and DNN score to be obtained within one month oh HBA1c measurement

• The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based an average of two PPG measurements.

  Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported as a DNN score. The investigators will assess the DNN performance by the Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.

  PPG measurements and DNN score to be obtained within one month oh HBA1c measurement

• Assess the performance of the DNN score in different ethnicity and skin tones

  The investigators will aim to recruit individuals of different races/ethnicities and skin tones to assess the performance of the DNN score in different races/ethnicities.

  PPG measurements and DNN score to be obtained within one month oh HBA1c measurement

Secondary Outcomes (3)

• The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements.

  PPG measurements and DNN score to be obtained within one month oh HBA1c measurement

• The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements.

  PPG measurements and DNN score to be obtained within one month oh HBA1c measurement

• Retrain the DNN algorithm

  Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.

Study Arms (2)

Study Population

EXPERIMENTAL

The investigators will conduct an electronic medical record (EMR) query of individuals in the University of California, San Francisco (UCSF) primary care clinics without a prior diagnosis of DM and who are undergoing, or who have recently undergone, a lab measured HBA1c before or after 1 month of enrollment. sample size estimation for testing the estimated AUROC in the validation sample vs. the null value of AUC 0.7. The investigators will target an enrollment of 5006 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.07 (i.e. AUROC = 0.76 \[95%CI 0.725, 0.795\]). The investigators assume that \~4% of the cohort will have undiagnosed diabetes based on national prevalence estimates.

Device: Application Validation

Alternative Sample Group

EXPERIMENTAL

The investigators also aim to perform a sensitivity analysis to estimate the DNN performance in a target general population without a diabetes diagnosis. The investigators will recruit patients from the UCSF EHR system without a history of diabetes, no prior HBA1c measured, and no history of known diabetic risk factors. The investigators will target an enrollment of 1000 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.18 (i.e. AUROC = 0.76 \[95%CI 0.67, 0.85\]). The investigators assume that \~3% of the cohort will have undiagnosed diabetes based on national prevalence estimates.

Device: Application Validation

Interventions

Application Validation DEVICE

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 .

Alternative Sample Group; Study Population

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age \> 18 years old
• Participants without a prior diagnosis of DM
• Participants with a recently measured HBA1c one month before enrollment or scheduled to undergo a HBA1c measurement within one month after enrollment
• Participants not scheduled for HBA1c and are willing to undergo a lab measured HBA1c
• Participants without risk factors for DM
• Participants with \> 1 of the following risk factors for DM:
• Age \> 40 years old
• Obesity (BMI \> 30)
• Family history: Any first degree relative with a hx of DM
• Lifestyle risk factors (exercise, smoking, and sleep duration)
• Ownership of a smart phone
• Able to provide informed consent
• Willingness to provide PPG waveforms

You may not qualify if:

• Participants with a history of DM
• Participants with a prior HBA1c \> 6.5%
• Inability to collect PPG signals (digit amputation, excessive tremors, etc)
• Lack of ownership of a smartphone
• Inability or unwillingness to consent and/or follow requirements of the study

Sponsors & Collaborators

• University of California, San Francisco: lead
• Azumio Inc.: collaborator
• Bristol-Myers Squibb: collaborator

Study Sites (1)

University of California, San Francisco

San Francisco, California, 94143, United States

Location

Related Publications (1)

• Avram R, Olgin JE, Kuhar P, Hughes JW, Marcus GM, Pletcher MJ, Aschbacher K, Tison GH. A digital biomarker of diabetes from smartphone-based vascular signals. Nat Med. 2020 Oct;26(10):1576-1582. doi: 10.1038/s41591-020-1010-5. Epub 2020 Aug 17.

  PMID: 32807931 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Officials

• Geoff Tison, MD, MPH

  University of California, San Franscisco

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 10, 2022
• First Posted: March 31, 2022
• Study Start: June 1, 2023
• Primary Completion: June 1, 2023
• Study Completion: April 1, 2025
• Last Updated: April 8, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)