DEN140016 · Dexcom, Inc. · PHV · Aug 19, 2014 · Clinical Chemistry
Device Facts
Record ID
DEN140016
Device Name
STUDIO ON THE CLOUD DATA MANAGEMENT SOFTWARE
Applicant
Dexcom, Inc.
Product Code
PHV · Clinical Chemistry
Decision Date
Aug 19, 2014
Decision
DENG
Submission Type
Direct
Regulation
21 CFR 862.2120
Device Class
Class 1
Attributes
Software as a Medical Device
Indications for Use
The STUDIO on the Cloud Data Manager Software is intended for use by both patients and healthcare professionals to assist people with diabetes and their healthcare professionals in the review, analysis and evaluation of historical CGM data to support effective diabetes management. It is intended for use as an accessory to CGM devices with data interface capabilities.
Device Story
STUDIO on the Cloud Data Management Software; cloud-based system for retrospective analysis of CGM data. Inputs: historical data from CGM devices with data interface capabilities. Operation: software processes and correlates retrospective glucose data; provides visualization and analysis tools for patients and clinicians. Output: reports/visualizations of historical glucose trends. Usage: home and clinical settings; operated by patients and healthcare providers. Benefit: assists in diabetes management by facilitating review of historical glucose patterns; does not provide real-time treatment recommendations or act as a drug dose calculator.
Clinical Evidence
Usability study conducted with 44 lay and professional users to verify software ease of use and label comprehension; 96% of assigned tasks completed without assistance. Bench testing performed using data from 40 G4 PLATINUM receivers; data uploaded via STUDIO software compared to direct PC downloads showed 100% accuracy in all data fields.
Technological Characteristics
Data management software platform; operates on Mac/PC; interfaces with CGM receivers via USB/data cable. Implements data validation, aggregation, and statistical analysis algorithms. Complies with ISO 13485, ISO 14971, and IEC 62304 standards. Cybersecurity controls include NIST SP 800-53 rev3 and HIPAA compliance. Software architecture includes data analysis/storage platform, report generation, and information delivery service.
Indications for Use
Indicated for patients with diabetes and their healthcare professionals to review, analyze, and evaluate historical continuous glucose monitoring (CGM) data to support diabetes management.
Regulatory Classification
Identification
A continuous glucose monitor data management system is an electronic device intended to acquire, process, and correlate retrospective data from a continuous glucose monitoring device. This device is intended to be used by patients or their healthcare providers when determining therapeutic strategies. A continuous glucose monitor data management system is not a drug dose calculator and does not provide treatment recommendations.
Submission Summary (Full Text)
{0}------------------------------------------------
## EVALUATION OF AUTOMATIC CLASS III DESIGNATION FOR STUDIO on the Cloud Data Management Software
## DECISION SUMMARY
#### A. DEN Number:
DEN140016
#### B. Purpose for Submission:
De novo request for adjunct data management software
### C. Measurand:
Not applicable. The submission is for a continuous glucose monitor data management software device.
#### D. Type of Test:
Diabetes data management system
## E. Applicant:
Dexcom, Inc.
#### F. Proprietary and Established Names:
STUDIO on the Cloud Data Management Software
# G. Regulatory Information:
- 1. Regulation: 21 CFR 862.2120, Continuous glucose monitor data management system.
- 2. Classification: Class I, exempt
- PHV 3. Product code:
- 4. Panel: Chemistry (75)
#### H. Intended Use:
- 1. Intended use(s):
{1}------------------------------------------------
The STUDIO on the Cloud Data Manager Software is intended for use by both patients and healthcare professionals to assist people with diabetes and their healthcare professionals in the review, analysis and evaluation of historical CGM data to support effective diabetes management. It is intended for use as an accessory to CGM devices with data interface capabilities.
- 2. Indication(s) for use:
Same as intended use
- 3. Special conditions for use statement(s):
For prescription home use.
This device is intended for display of retrospective glucose data and not for real-time display of glucose results.
This device is not intended for making treatment decisions.
This device is not intended for calculating insulin or other drug doses.
This device is not intended for controlling insulin pumps or other drug delivery systems.
- 4. Special instrument requirements:
Dexcom G4 PLATINUM Continuous Glucose Monitoring System
### I. Device Description:
The STUDIO on the Cloud Data Management ("STUDIO") Software is comprised of a data analysis and storage platform, report generation software, and an information delivery service.
Specifically, the proposed STUDIO Software performs the following functions:
- Data Upload: the SweetSpot Fetch Utility application will be used to access data from ● a Receiver, using either Mac or PC operating systems;
- Data Analysis: certain SweetSpot Platform functions will be used to validate. ● aggregate, and analyze (e.g., correlate) CGM data, and to create charts and reports that mimic the current STUDIO Pattern and Glucose Strips charts and reports;
- . Reports: The current STUDIO Pattern charts will be displayed on the user's computer screen, and both the Pattern and Glucose Strip charts can be saved to the user's computer in PDF format. Both reports may be printed by the user as a PDF document.
The STUDIO Software uses only retrospective data stored on the G4 PLATINUM device to create statistical reports, and does not make treatment recommendations.
{2}------------------------------------------------
### J. Standard/Guidance Documents Referenced:
- 1. Guidance for Industry, FDA Reviewers and Compliance on Off-The-Shelf Software Use in Medical Devices, September 9, 1999
- 2. Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, May 11, 2005
- 3. Content of Premarket Submissions for Management of Cybersecurity in Medical Devices, Draft Guidance for Industry & FDA Staff, June 13, 2013
- 4. Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software, January 14, 2005
- 5. General Principles of Software Validation, Final Guidance for Industry and FDA Staff, January 11, 2002
- 6. Guidance for Industry and Food and Drug Administration Staff, Factors to Consider When Making Benefit-Risk Determinations in Medical Device Premarket Approval and De Novo Classifications, March 28, 2012
- 7. ISO 14971:2012 Medical devices Application of risk management to medical devices
- 8. IEC/TR 80002-1:2009 Medical device software Part 1: Guidance on the application of ISO 14971 to medical device software
- 9. ANSI/AAMI/IEC 62304:2006 Medical device software Software life cycle processes
- 10. ISO 13485: Quality Systems Medical Devices System Requirements for Regulatory Purposes
- 11. U.S. Food and Drug Administration, 21 CFR Part 820: Quality System Regulation
- 12. NIST SP 800-53 rev3: Recommended Security Controls for Federal Information Systems and Organizations
- 13. HIPAA: Health Insurance Portability and Accountability Act
### K. Test Principle:
Not applicable.
### L. Performance Characteristics (if/when applicable):
- 1. Analytical performance:
- a. Reproducibility/Precision
Not applicable.
- b. Linearity/assay reportable range:
{3}------------------------------------------------
Not applicable.
- c. Traceability, Stability, Expected values (controls, calibrators, or methods): Not applicable.
- d. Detection limit
Not applicable.
- e. Analytical specificity:
Not applicable.
- 2. Comparison studies:
- a. Method comparison with predicate device:
Not applicable.
- b. Matrix comparison:
Not applicable.
- 3. Clinical studies:
A usability study was performed with Forty Four (44) lay and professional users with varying demographic characteristics (age, sex, and education level). The intent of the study was to verify software ease of use and label comprehension. The study determined that 96% of assigned tasks were able to be completed by users without assistance.
- 4. Expected Values
Not applicable.
### M. Instrument Names:
STUDIO on the Cloud Data Management Software
### N. System Description:
- 1. Modes of Operation:
Does the applicant's device contain the ability to transmit data to a computer, webserver, or mobile device? Yes _X__ or No _
{4}------------------------------------------------
Does the applicant's device transmit data to a computer, webserver, or mobile device using wireless transmission: Yes or No X > > .
- 2. Software:
FDA has reviewed applicant's Hazard Analysis and software development processes for this line of product types:
Yes ____X_____ or No _________________________________________________________________________________________________________________________________________________________
- 2. Specimen Identification:
Not Applicable
- 3. Specimen Sampling and Handling:
Not Applicable
- 4. Calibration:
Not Applicable
- 5. Quality Control:
Not Applicable
#### O. Other Supportive Instrument Performance Characteristics Data Not Covered In the "Performance Characteristics" Section above:
- 1. The following documentation related to the STUDIO on the Cloud Data Management Software was reviewed and found to be acceptable: level of concern, software description, device hazard analysis, software requirements specifications, architecture design chart, software design specification, traceability analysis, software development environment description, verification and validation testing, and revision level history.
- 2. Bench Testing was perfomed using data from forty (40) G4 PLATINUM receivers. CGM data were uploaded from the receivers using the STUDIO on the Cloud Data Management Software and were compared to the same data downloaded to a PC. All data fields were reported to be 100% accurate.
#### P. Proposed Labeling:
The labeling is sufficient and satisfies the requirements of 21 CFR Part 801, 21 CFR Part 809, and 801.109.
{5}------------------------------------------------
### Q. Identified Potential Risks and Required Mitigations
| Identified Risk | Required Mitigation |
|-------------------------------------------------------------|---------------------------------------------|
| Device malfunction (e.g., incorrect data<br>analysis, etc.) | General controls, including design controls |
Identified Risks and Required Mitigations Table
Device malfunction (e.g., incorrect data analysis, etc.) may lead to diabetes mismanagement and poor glycemic control. This risk can be adequately mitigated by general controls, including design controls and restriction as a prescription device that must comply with 21 CFR 801.109.
# R. Benefit/Risk Analysis
| Summary | |
|----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Summary of<br>the Benefit(s) | The STUDIO on the Cloud Data Management Software is convenient to use since it<br>analyzes and correlates several sources of diabetes management-relevant information<br>(e.g. user glucose levels, meals, insulin delivery, and exercise data) into one software<br>program. The data sorting and presentation functions of the STUDIO on the Cloud<br>Data Management Software provide patients and their doctors access to a more<br>complete clinical picture of a patient's current disease, as well as of the impact of past<br>diabetes management decisions on a patient's glucose levels. The convenience of<br>using the device should translate into better record-keeping compliance by the patient,<br>and the greater access provided to data should assist the patient and their physician<br>with the identification of the patient's unique glucose excursion triggers; together,<br>these benefits should allow patients and their doctors to make rational modifications to<br>the patient's diabetes management plan, with the goal of achieving better glycemic<br>control. |
| Summary of<br>the Risk(s) | Device malfunction may lead to diabetes mismanagement and poor glycemic control.<br>Decisions made based on incorrect data or faulty analyses may put the patient at risk of more frequent acute episodes of hypoglycemic and/or hyperglycemic excursions.<br>These episodes increase the likelihood of hospitalization and/or death. Chronic poor glycemic control could lead to irreversible diabetes-related sequelae (e.g. retinopathy, neuropathy, nephropathy and arteriosclerosis). These risks can be adequately mitigated by the sponsor's verification and validation and design control activities which ensure that the risk of malfunction is very low.<br><br>Continuous glucose meters are only approved for tracking and trending; therefore another risk is that users could modify their current insulin dosage based directly on current CGM glucose values provided by the STUDIO on the Cloud Data Management Software. This risk is mitigated by product labeling which states that users should not make changes in their treatment program without talking to their healthcare providers. In addition, only retrospective CGM glucose values are provided by the software, so real-time CGM glucose values are not readily available to users. Risks are mitigated by general controls, including requiring design controls and restriction as a prescription device that must comply with 21 CFR 801.109. |
| Summary of<br>Other Factors | Patients are willing to tolerate the low risk associated with use of the STUDIO on the Cloud Data Management Software because they benefit from a substantial improvement in the analysis and correlation of retrospective continuous glucose monitoring information (e.g. glucose values over time, paired with meal, exercise, and insulin bolus information), which can be used by the patients and their doctors to assist in making adjustments to their diabetes management program, with the goal of reducing glucose excursions and maintaining proper glycemic control. |
| Conclusions<br>Do the<br>probable<br>benefits<br>outweigh the<br>probable risks? | Yes. The device is likely to provide benefits in improved diabetes management with a low associated risk. |
{6}------------------------------------------------
### S. Conclusion:
The information provided in this de novo submission is sufficient to classify this device into class I, exempt from premarket notification requirements subject to the limitations in 21 CFR 862.9, under regulation 21 CFR 862.2120. As a software containing device, this device type is also subject to design controls. FDA believes that applicable general controls, including design controls, provide reasonable assurance of the safety and effectiveness of the device type. The device is classified under the following:
{7}------------------------------------------------
| Product Code: | PHV |
|---------------|---------------------------------------------------|
| Device Type: | Continuous glucose monitor data management system |
| Class: | I (general controls) |
| Regulation: | 21 CFR 862.2120 |
(a) Identification. A continuous glucose monitor data management system is an electronic device intended to acquire, process, and correlate retrospective data from a continuous glucose monitoring device. This device is intended to be used by patients or their healthcare providers when determining therapeutic strategies. A continuous glucose monitor data management system is not a drug dose calculator and does not provide treatment recommendations.
(b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.