DiabetesNCT05303051
Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
A study testing Application Validation in people with Diabetes, run by University of California, San Francisco at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diabetes
- What is being tested?
- Application Validation
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 0 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Jun 2023to 1 Apr 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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:
Show all 13Show fewer
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
United States1 site
University of California, San Francisco
San Francisco, California, 94143
About this study
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
- Being tested
- Application Validation
- Medical subject headings
- Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Application ValidationDevice
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 .
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Device: Application Validation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
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 the area under the receiver operating characteristic (AUROC) of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
Measured: 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.
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 Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
Measured: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Retrain the DNN algorithm
By collecting PPG waveform data in patients with laboratory-confirmed diabetes, the investigators will be able to train the algorithm using the more specific diagnosis of laboratory-confirmed diabetes. The investigators will assess the performance of the DNN Score once retrained using HbA1c. The DNN will be trained using similar approaches as the investigators have previously published
Measured: Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2022
- Study start1 Jun 2023 (estimated)
- Primary completion1 Jun 2023
- Study completion1 Apr 2025
Why it stopped: Mobile app incompatibility prevented the study from enrolling.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DiabetesStudies of the same condition, open and under way.
- More trials run by University of California, San FranciscoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Collaborators
- Azumio Inc., Bristol-Myers Squibb
- Responsible party
- Sponsor
- Investigators
- Geoff Tison, MD, MPH, Principal investigator, University of California, San Franscisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05303051
- Sponsor's protocol ID
- 21-35207
- Status last verified
- Apr 2025
- First submitted
- 10 Mar 2022
- Last updated
- 8 Apr 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
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.
PubMed 32807931
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
- ClinicalTrials.gov ID
- NCT05303051
- Acronym / sponsor ID
- 21-35207
- Conditions
- Diabetes
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: Mobile app incompatibility prevented the study from enrolling.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Collaborators
- Azumio Inc., Bristol-Myers Squibb
- Study start
- 1 Jun 2023 (estimated)
- Primary completion
- 1 Jun 2023 (actual)
- Study completion
- 1 Apr 2025 (actual)
- First posted
- 31 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Study Population | Experimental | Device: Application ValidationThe 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. |
| Alternative Sample Group | Experimental | Device: Application ValidationThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (13 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Application ValidationAfter 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 . | Device | Alternative Sample Group, Study Population |
Primary outcome
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.
Time frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
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.
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.
Time frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
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.
Assess the performance of the DNN score in different ethnicity and skin tones
Time frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
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.
3 secondary outcomes
The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements.
Time frame: 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.
Time frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Retrain the DNN algorithm
Time frame: Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2025.
- Background: PubMed 32807931Avram 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.
- Individual participant dataSharing planned: No
Related trials for Diabetes
Not yet recruitingPhase 3
Efficacy and Safety of Once-Weekly SHR-3167 Compared With Once-Daily Insulin Glargine in Adults With Insulin-Naive Type 2 Diabetes
Type 2 Diabetes
Active, not recruiting
Digital Reinforcement of Motivational Interviewing Micro-Skills Among Primary Care Healthcare Professionals
Hypertension (Without Type 2 Diabetes Mellitus)
Recruiting
Effects of Lean Pork Loin Intake on Protein Homeostasis and Glucose Regulation in Prediabetic Adults
Hyperglycaemia (Non Diabetic), Prediabetes
Recruiting
PAW-Hydrogel for Chronic Wound Healing
Diabetic Foot Ulcer (DFU), Pressure Ulcer (PU)
Recruiting
Investigating Vascular Properties of HEMI and SPG Signals in Individuals With or at Risk for Chronic Kidney Disease
Chronic Kidney Disease, Hypertension (HTN)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Geoff Tison, MD, MPH principal investigator
University of California, San Franscisco
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Application Validation in people with Diabetes, run by University of California, San Francisco at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 13 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 8 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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. (time frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05303051, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2025.