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The Signos Glucose Monitoring System is an over-the-counter (OTC) mobile device application that receives data from an integrated Continuous Glucose Monitor (iCGM) sensor and is intended to continuously measure, record, analyze, and display glucose values in people 18 years and older not on insulin. The Signos Glucose Monitoring System helps to detect normal (euglycemic) and low or high (dysglycemic) glucose levels. The Signos Glucose Monitoring System may also help the user better understand how lifestyle and behavior modification, including diet and exercise, impact glucose excursions. This information may be useful in helping users to maintain a healthy weight.

The user is not intended to take medical action based on the device output without consultation with a qualified healthcare professional.

The Signos Glucose Monitoring System is a mobile device application that is paired, via Bluetooth®, with an over-the-counter interoperable continuous glucose monitor (iCGM). The application functions as a primary display for the iCGM by showing the user's glucose reading along with a historic trend every 15 minutes. The system is capable of backfilling missed data and supporting a grace period dictated by the iCGM.

The system's various displays, text, graphs, suggestions, and notifications serve to clearly illustrate the user's past and present glucose readings and their trend direction to assist the user in maintaining a euglycemic state.

The glucose display range is 70 mg/dL to 250 mg/dL.

The Signos System is intended for users over the age of 18 not on insulin.

The provided text is a 510(k) premarket notification letter from the FDA regarding the Signos Glucose Monitoring System. It primarily focuses on the device's substantial equivalence to a predicate device based on its intended use, technological characteristics, and non-clinical testing.

Unfortunately, the provided document does not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets those criteria, specifically for performance metrics like accuracy or effectiveness related to AI/algorithm performance. The document is a regulatory clearance letter, not a detailed study report.

Here's what can be inferred from the document and what information is missing:

What the document does provide:

• Device Name: Signos Glucose Monitoring System
• Intended Use: Over-the-counter (OTC) mobile device application that receives data from an integrated Continuous Glucose Monitor (iCGM) sensor. Intended to continuously measure, record, analyze, and display glucose values in people 18 years and older not on insulin. Helps detect normal/low/high glucose levels and understand how lifestyle impacts glucose excursions. Not intended for medical action without consultation.
• Technological Characteristics: Software system, displays interstitial fluid glucose sensor data, assists in understanding lifestyle impact on glucose. Uses data from an iCGM (same as predicate). Display range: 70-250 mg/dL. Update interval: Every 15 minutes.
• Non-Clinical Testing Mentioned:
  • Software Testing: Verified that the system functions consistently with design inputs and that displayed data is the same as transmitted data. (This is a functional verification, not a performance study against specific acceptance criteria for diagnostic accuracy)
  • Cybersecurity Testing: Demonstrated no unacceptable cybersecurity risks.
  • Usability / Human Factors: Demonstrated unacceptably low risks related to use errors that could cause harm or degrade performance.

What the document does not provide, and therefore cannot be filled:

1. A table of acceptance criteria and the reported device performance: The document mentions "software requirements have been verified," but does not list specific performance acceptance criteria for glucose measurement accuracy (e.g., MARD, Clarke Error Grid analysis) or how the algorithm detects normal/low/high glucose levels beyond simply displaying the iCGM data. It states the displayed data is the same as transmitted, implying the software's role is primarily display and analysis, not independent glucose measurement.
2. Sample size used for the test set and the data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable based on the information provided, as the software is stated to display data transmitted by the biosensor, not to perform independent diagnostic interpretations requiring expert ground truth.
4. Adjudication method: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not mentioned and unlikely given the device's described function as a display and analysis tool for iCGM data, rather than an AI diagnostic aid for image interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document describes "Software Testing" which confirms the software displays data correctly, but this is not a standalone diagnostic performance study using an algorithm to interpret data independently of the iCGM. The device's "algorithm" here seems to be in the display and analysis of iCGM data (e.g., trend direction, identifying dysglycemic states based on thresholds), not in generating novel glucose measurements.
7. The type of ground truth used: Not explicitly stated for any actual performance metrics. The software testing confirmed data consistency with the biosensor, implying the biosensor's output is the "truth" for the software. For general "detection of euglycemic/dysglycemic" states, presumably standard glucose thresholds would be used.
8. The sample size for the training set: Not mentioned. The document describes software verification, cybersecurity, and human factors testing, not machine learning model training and validation.
9. How the ground truth for the training set was established: Not mentioned.

Conclusion:

The provided FDA letter grants marketing clearance based on substantial equivalence, primarily asserting that the Signos Glucose Monitoring System is a mobile application that accurately displays data from a legally marketed and cleared iCGM. It emphasizes software functionality, cybersecurity, and usability rather than presenting de novo clinical performance data for an AI/algorithm that performs diagnostic interpretations. The letter likely relies on the predicate iCGM's established performance for glucose measurement, with the Signos system's "performance" being its accurate receipt, display, and basic analysis of that underlying data. Therefore, the detailed performance data and acceptance criteria typical for AI-driven diagnostic devices are not present in this regulatory clearance document.

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