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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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The Lingo Glucose System is an over-the-counter (OTC) integrated Continuous Glucose Monitor (iCGM) intended to continuously measure, record, and display glucose values in people 18 years and older not on insulin. The Lingo Glucose System helps to detect euglycemic glucose levels. The Lingo Glucose System may also help the user better understand how lifestyle and behavior modification, including diet and exercise, impact glucose excursion.

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

The Lingo Glucose System (also referred to as 'System') is Abbott's latest biowearable evolution in health technology for glucose measurement. This system encourages users 18 years and older not on insulin, to understand how glucose impacts their body. The Lingo Glucose System includes the Lingo Glucose Biosensor and the Lingo App.

Lingo Biosensor: The Lingo Glucose Biosensor hardware and technology is based on the FDA-cleared FreeStyle Libre 2 (FSL2) sensor (K222447). The Biosensor is a single use disposable on-body Biosensor that incorporates a subcutaneously implanted electrochemical glucose sensor and associated electronics. The Biosensor can be worn for up for 14 days and transmits data to the Lingo App via Bluetooth Low Energy (BLE). Similar to the predicate device, a disposable Biosensor insertion device, consisting of a Biosensor Applicator and Biosensor Pack is used to assemble and apply the Biosensor to the back of the user's upper arm.

Lingo App (iOS): When downloaded on a compatible smartphone running on iOS, the Lingo App uses Near-Field Communication (NFC) to start a new Biosensor and uses BLE to receive glucose data from the Biosensor. The user can view real-time glucose value, trend arrow, and glucose graph on the app through a glucose range of 55-200 mg/dL. The Lingo App contains on-boarding materials with a self-selection questionnaire that a user must consent prior to using the device. The App does not provide any glucose or system alerts.

The Lingo Glucose System (K233655) is an over-the-counter (OTC) integrated Continuous Glucose Monitor (iCGM) intended for continuous measurement, recording, analysis, and display of glucose values in people 18 years and older not on insulin. It aims to help users detect euglycemic and dysglycemic glucose levels and understand the impact of lifestyle modifications on glucose excursions.

Here's an analysis of its acceptance criteria and the study used to demonstrate fulfillment:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA clearance relies on the substantial equivalence of the Lingo Glucose System to its predicate device, the FreeStyle Libre 2 Flash Glucose Monitoring System (K222447). The clinical performance acceptance criteria for the Lingo Glucose System are tied to meeting the iCGM special controls requirements per 21 CFR 862.1355. While specific numerical acceptance criteria for accuracy (e.g., MARD percentage) are not explicitly stated in the provided text, the documentation states that the device demonstrated accuracy (clinical performance) meeting these iCGM special controls.

2. Sample Size Used for the Test Set and Data Provenance

The Lingo Glucose System leverages the clinical data from the FreeStyle Libre 2 (FSL2) study (K222447). The text states: "Abbott conducted a statistical analysis to confirm that the clinical data of the FSL2 System (submitted under K222447) can be leveraged to support the Lingo Glucose System."

• Sample Size for Test Set: Not explicitly stated for the FSL2 study in this document.
• Data Provenance: Not explicitly stated in this document. Based on typical FDA submissions for iCGM devices and considering the predicate device (FreeStyle Libre 2), these trials are generally prospective and multi-center, often involving participants from various healthcare systems or regions.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the given text. For iCGM studies, ground truth is typically established by laboratory reference methods (e.g., YSI glucose analyzer) rather than expert consensus on images or clinical assessments.

4. Adjudication Method for the Test Set

This information is not provided in the given text. Again, for iCGM studies, the reference method provides the ground truth, so expert adjudication methods (like 2+1 or 3+1 used in imaging studies) are typically not applicable to the establishment of the ground truth itself.

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

An MRMC study is not applicable to this device. The Lingo Glucose System is a continuous glucose monitor (CGM) and does not involve human "readers" interpreting medical images or data in a way that would necessitate an MRMC analysis of AI assistance. Its primary function is direct glucose measurement.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, the clinical performance assessment of the Lingo Glucose System (by leveraging the FSL2 data) effectively represents a standalone algorithm performance study relative to the reference glucose measurements. The "system accuracy was demonstrated to meet the iCGM special controls requirements." The device continuously measures and reports glucose values, which is an algorithmic output compared against a reference standard. The user interacts with the app to view these values, but the core accuracy is an algorithmic function.

7. The Type of Ground Truth Used

The ground truth for iCGM devices is almost universally established by laboratory reference methods for glucose measurement, such as a YSI glucose analyzer, from blood samples drawn contemporaneously with the interstitial fluid measurements. The text refers to "clinical data" and "sensor performance," implying a comparison against such a gold standard.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It mentions that "ADC Glucose Algorithm established for the predicate device" is used for the Lingo Glucose System. The development and training of such an algorithm would have involved a substantial dataset, but the specifics are not detailed here.

9. How the Ground Truth for the Training Set Was Established

The document states that the "ADC Glucose Algorithm established for the predicate device" is used. For this type of algorithm, the ground truth for its development (training) would have been established through a combination of:

• Laboratory reference glucose measurements: From blood samples.
• Contemporaneous interstitial fluid readings: From prototype or earlier versions of the sensor.
• Extensive data collection: From a diverse population under various physiological conditions (e.g., different glucose levels, meals, exercise).

This data would be used to develop and refine the algorithm that translates the electrochemical signals from the sensor into accurate glucose readings.

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The Stelo Glucose Biosensor System is an over-the-counter (OTC) integrated Continuous Glucose Monitor (CCGM) intended to continuously measure, record, analyze, and display glucose values in people 18 years and older not on insulin. The Stelo Glucose Biosensor System helps to detect normal (euglycemic) and low or high (dysglycemic) glucose levels. The Stelo Glucose Biosensor System may also the user better understand how lifestyle and behavior modification, including diet and exercise, impact glucose excursion.

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

The Stelo Glucose Biosensor system (Stelo System) is an over-the-counter (OTC) interoperable continuous glucose monitoring (iCGM) system.

The Stelo Glucose Biosensor system (Stelo System) is an interoperable connected device that measures and displays estimated glucose values for people who are not on insulin. The Stelo System consists of the following components: the Glucose Sensing Subsystem (GSS) and the Mobile Application Subsystem (MAS). The GSS is comprised of the sensor applicator and on-body wearable, which includes a Bluetooth Low Energy (BLE) molded transmitter, adhesive patch and sensor. The sensor is a small and flexible wire, which is inserted by the applicator into subcutaneous tissue where it converts glucose into electrical current. The transmitter's onboard algorithm converts these measurements into estimated glucose values and calculates the glucose rate of change which are sent every 5 minutes to the MAS is an app that can be downloaded to a compatible smart device and that presents glucose readings and qlucose trend to the user every 15 minutes. As such, the most recent displayed glucose value might be up to 15 minutes old. Each sensor session lasts up to 15 days with an extended 12-hour grace period. The grace period allows additional time for the user to change the sensor at a convenient time.

The proposed Stelo System is based on the same mode of operation and mechanism of reaction as the predicate G7 CGM System (K231081), which uses a wire type sensing mechanism that continuously measures interstitial fluid qlucose levels and a BLE enabled radio transmitter to wirelessly communicate CGM data to compatible display devices at regular 5-minute intervals. These data are also able to be reliably and securely transmitted to other digitally connected devices, excluding insulin pens and Automated Insulin Dosing (AID) systems.

The Stelo System uses the same hardware design as the predicate G7 CGM System. The Stelo GSS firmware is designed to support a factory-calibrated only device (without calibration inputs), to extend the sensor wear duration from 10 to 15 days while maintaining the accuracy of the device, and to connect to authorized display devices only (i.e., Stelo MAS). The Stelo MAS includes a redesigned user interface (UI) tailored to the Stelo System's user population to simplify the use of the device. The UI includes an app onboarding specific to the Stelo MAS design and its intended use, the most recent glucose value and trend graph which are updated every 15 minutes, a narrowed glucose range display from 70 mg/dL, and an "Insights" feature providing the time in range percentage with suggestions to help users improve their time in range. The Ul does not provide any glucose or system alerts.

The Dexcom Stelo Glucose Biosensor System is a continuous glucose monitoring (CGM) device intended for over-the-counter (OTC) use by adults aged 18 and older who are not on insulin. The device continuously measures, records, and displays glucose values, helping to detect normal, low, or high glucose levels and allowing users to understand the impact of lifestyle modifications on glucose excursion.

Here's a breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document explicitly states that the device meets the iCGM special controls for clinical performance set forth in 21 CFR 862.1355. While specific numerical acceptance criteria (e.g., MARD values, percentages of readings within certain zones) are not provided in the summary, the general umbrella acceptance criterion is:

2. Sample Size Used for the Test Set and Data Provenance:

The document states: "A clinical study was conducted to evaluate the safety and effectiveness of the Stelo Glucose Biosensor System. The effectiveness of the device was evaluated with respect to reference venous plasma sample YSI measurements across the measuring range throughout a 15-day wear duration with a 12-hour grace period in adult (18 years and older) participants with diabetes."

• Test Set Sample Size: The exact number of participants is not specified, but it refers to "adult (18 years and older) participants with diabetes."
• Data Provenance: Prospective clinical study data. The country of origin is not explicitly stated in the provided text.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

The ground truth for the clinical study was established using "reference venous plasma sample YSI measurements." This usually refers to laboratory-based glucose analysis performed by trained technicians, not "experts" in the context of interpretation (like radiologists). The number and qualifications of technicians are not mentioned.

4. Adjudication Method for the Test Set:

Not applicable, as the ground truth was established through laboratory reference measurements (YSI) of venous plasma samples, which are generally considered the gold standard and do not typically require adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study was not done. The study evaluated the standalone performance of the Stelo Glucose Biosensor System against reference measurements. The document does not mention any human readers or human-AI interaction in the context of this study.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

Yes, a standalone study was done. The clinical study evaluated the Stelo Glucose Biosensor System's effectiveness by comparing its glucose measurements to reference venous plasma YSI measurements, implying an assessment of the device's algorithmic and sensing accuracy without human intervention for interpretation beyond what the user does with the displayed values.

7. The Type of Ground Truth Used:

The ground truth used was laboratory reference measurements ("reference venous plasma sample YSI measurements").

8. The Sample Size for the Training Set:

The document does not provide any information about the training set size for the algorithms within the Stelo Glucose Biosensor System. This information is typically not included in a 510(k) summary unless the submission is for an AI/ML SaMD where the training data characteristics are a primary focus. Given that the Stelo System uses the "same hardware design as the predicate G7 CGM System" and its GSS firmware is designed to support a "factory-calibrated only device (without calibration inputs)," it suggests that the core algorithms might have been trained previously and validated, or leveraged from the predicate device's development.

9. How the Ground Truth for the Training Set Was Established:

This information is not provided in the document.

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