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510(k) Data Aggregation
(184 days)
The Libre Rio Continuous Glucose Monitoring System is an over-the-counter (OTC) integrated continuous glucose monitoring (iCGM) device indicated for non-insulin using persons age 18 and older. The System detects trends and tracks patterns and aids in the detection of euglycemia, and hypoglycemia. The System is also intended to autonomously communicate with digitally connected devices.
The Libre Rio Continuous Glucose Monitoring System (herein referred to as the 'System') is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to facilitate calculation of glucose values accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The System is intended for over-the-counter use in a home setting. The System consists of the following components: a Sensor which transmits via Bluetooth Low Energy (BLE), and a mobile application Libre Rio App that is downloaded to a compatible smartphone running iOS and Android operating system.
Here are the details regarding the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The provided document does not explicitly present a table of acceptance criteria with corresponding performance metrics for the Libre Rio Continuous Glucose Monitoring System. It primarily focuses on the device's substantial equivalence to a predicate device and summarizes various performance testing categories. The "Reported Device Performance" for each category usually states that acceptance criteria were met, without detailing the specific metrics or thresholds.
However, based on the type of testing mentioned and common practices for CGM devices, we can infer some general areas where acceptance criteria would have been established and reportedly met:
| Area of Performance Testing | Implied Acceptance Criteria (General) | Reported Device Performance (Summary) |
|---|---|---|
| Software Verification & Validation | Adherence to IEC 62304 and FDA guidance for software functions, no critical bugs, software performs as intended. | "Results of executed protocols met the acceptance criteria and therefore support that the System software is acceptable for its intended use." |
| Cybersecurity | Adherence to FDA guidance for cybersecurity in medical devices, identification and mitigation of threat/vulnerability risks, protection of confidentiality, integrity, and availability of data. | "Appropriate risk mitigation controls have been implemented and tested." |
| Interoperability | Compliance with FDA guidance for interoperable medical devices, successful communication with digitally connected devices. | Approach "developed in alignment with FDA guidance," implying successful implementation. |
| Human Factors | Adherence to ANSI/AAMI/IEC 62366, IEC 60601-1-6, and FDA guidance for human factors, demonstrating usability and safety for intended users. | "The analysis and the study performed demonstrated that the changes implemented for the subject device meet the usability requirement for its intended use." |
| Bench Testing (CT, MRI, X-ray compatibility) | Device maintains functionality and accuracy under specific CT, MRI, and X-ray conditions; no adverse effects. | "The test results showed all functionality testing acceptance criteria was met." |
| Biocompatibility, Sterility, Shelf Life, Packaging, Electrical Safety, EMC, Mechanical Design, Clinical Performance | Each established for the predicate device, implying they met relevant standards and criteria. | "The Libre Rio Sensor is identical to the predicate FreeStyle Libre 2 Sensor and no design changes were introduced to allow compatibility to the Libre Rio App. Therefore, the following supportive performance characteristics established for the predicate device (K222447) is applicable to the subject device and is not impacted." These were implicitly met by the predicate and thus deemed met for the subject device. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample sizes for the test sets used in the listed performance tests. It mentions "studies" and "testing" but does not quantify the number of participants or data points.
The data provenance is not explicitly mentioned for the reported tests. However, the study is for the Libre Rio Continuous Glucose Monitoring System, which appears to be a new device (or an updated version) from Abbott Diabetes Care, Inc., located in Alameda, CA, USA. This suggests the data would likely be generated in the USA, and primarily from prospective testing conducted specifically for this submission, especially for areas like human factors, bench testing for new contraindications, and software validation. The clinical performance data is stated to be derived from the predicate device (K222447), which would have its own provenance details.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not provide information on the number of experts used or their qualifications for establishing ground truth for any of the mentioned test sets. It broadly refers to "studies" and "testing" but does not detail the methodology of ground truth establishment for specific components like software, cybersecurity, or human factors.
4. Adjudication Method for the Test Set
The document does not specify any adjudication methods (e.g., 2+1, 3+1, none) used for establishing ground truth in the performance testing.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
No, the document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The device is a continuous glucose monitoring system, which typically involves direct measurement and user interaction, not interpretation by multiple human readers of clinical cases. The "Human Factors" testing mentioned focuses on usability and user interface, not on comparative effectiveness with human interpretation of readings.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, implicit in the description of the device and testing, especially for "Software Verification and Validation" and the "Sensor glucose algorithm," is that standalone algorithm performance testing was conducted. The device's "Sensor glucose algorithm" is stated to be the "ADC Glucose Algorithm established for the predicate device," which would have undergone rigorous standalone validation. The "Libre Rio App" is designed to autonomously receive and display glucose data, suggesting the algorithm operates independently of immediate human intervention for value generation. The bench testing of the sensor's compatibility with CT, MRI, and X-ray would also involve objective measurements without human interpretation in the loop to assess the sensor's function.
7. The Type of Ground Truth Used
The document does not explicitly state the type of ground truth used for each specific test. However, based on the nature of the device and the tests:
- Clinical Performance (inherited from predicate): For CGM devices, the gold standard for ground truth is typically blood glucose measurements obtained from a laboratory reference method (e.g., YSI analyzer) at various glucose levels.
- Software Verification and Validation: Ground truth would be based on functional specifications and expected outputs for given inputs.
- Cybersecurity: Ground truth involves adherence to security protocols and identified risk mitigations.
- Human Factors: Ground truth is established through user task completion rates, error rates, and subjective feedback against predefined usability goals and safety requirements.
- Bench Testing (CT, MRI, X-ray): Ground truth would be based on physical and electrical performance standards and expected device behavior under specific environmental conditions, potentially using calibrated instruments to verify sensor output accuracy.
8. The Sample Size for the Training Set
The document does not provide information about the sample size for the training set. Given that the sensor glucose algorithm is "ADC Glucose Algorithm established for the predicate device," the training data would be associated with the development of that original algorithm, not necessarily new training for the Libre Rio specifically (unless modifications were made, which is not indicated for the algorithm itself).
9. How the Ground Truth for the Training Set Was Established
The document does not specify how the ground truth for the training set (for the inherited "ADC Glucose Algorithm") was established. For glucose monitoring algorithms, ground truth for training data is typically established through paired comparisons with laboratory reference methods (e.g., YSI blood glucose measurements) across a diverse range of glucose values, patient populations, and physiological conditions.
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(172 days)
Libre 2 Sensor users:
The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.
Libre 2 Plus Sensor users:
The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.
Libre 3 Sensor users:
The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.
Libre 3 Plus Sensor users:
The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.
The FreeStyle Libre 3 Continuous Glucose Monitoring System (hereinafter also referred to as 'FSL3 System') and FreeStyle Libre 2 Flash Glucose Monitoring System (hereinafter also referred to as the 'FSL2 System') are integrated continuous glucose monitoring (iCGM) systems that provide continuous glucose measurements every minute to provide glucose levels, trends, and real-time alarms capability to aid in the management of diabetes. The FSL2 and FSL3 Systems also provide configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss. The user may make treatment decisions based in part on the sensor glucose results provided by both Systems. The FSL2 and FSL3 Systems require a prescription and are intended for home use.
The subject FSL3 System consists of a sensor (the FreeStyle Libre 3 Sensor (FSL3 Sensor) or FreeStyle Libre 3 Plus Sensor (FSL3 Plus Sensor)) and a primary display device (the FreeStyle Libre 3 Reader (FSL3 Reader) or the FreeStyle Libre App (FSL App) [iOS and Android] downloaded to a compatible phone). The FSL3 Reader and the FSL App do not interact with each other.
The subject FSL2 System consists of a sensor (the FreeStyle Libre 2 Sensor (FSL2 Sensor) or FreeStyle Libre 2 Plus Sensor (FSL2 Plus Sensor)) and a primary display device (the FreeStyle Libre 2 Reader (FSL2 Reader) or the FSL App (iOS and Android) downloaded to a compatible phone). The FSL2 Reader and FSL App do not interact with each other.
Both the FSL2 and FSL3 Systems are compatible with the Libre Data Sharing API cleared in K223537. The display device of the connected FSL2 or FSL3 Systems, which directly receives the data from the sensor, continues to serve as a primary display device for the glucose data and alarms.
This document is a 510(k) summary from Abbott Diabetes Care Inc. regarding their FreeStyle Libre 3 Continuous Glucose Monitoring System and FreeStyle Libre 2 Flash Glucose Monitoring System. It outlines the device details, indications for use, comparison to predicate devices, and a summary of performance testing.
Acceptance Criteria and Study Proving Device Meets Criteria
The provided text focuses on the substantial equivalence of the new FreeStyle Libre 2 Plus and 3 Plus sensors and updated app features to their predicate devices, rather than establishing new acceptance criteria for the entire system's glucose monitoring accuracy. The document states that clinical performance and human factors were "established in the predicate device (K223435) and are not affected by the introduction of the FSL App in this 510(k)." This implies that the glucose accuracy and user interface performance metrics (e.g., MARD, % in Zones A+B, usability outcomes) for the core glucose monitoring function were previously accepted for the predicate devices and are assumed to hold true for the current submission due to the nature of the changes being primarily related to sensor life, age ranges, and app features.
Therefore, the acceptance criteria and performance data discussed below are extrapolated from the types of performance studies conducted to demonstrate substantial equivalence for the modifications presented in this 510(k), particularly regarding safety and compatibility, and from the implied reliance on the predicate device's established clinical performance.
Please note: This response does not contain specific quantitative acceptance criteria or precise performance metrics for glucose accuracy (e.g., MARD values) as these details are explicitly referred to as having been established in the predicate device (K223435) and are not re-evaluated in this submission. The focus of this 510(k) is on the changes from the predicate.
1. Table of Acceptance Criteria and Reported Device Performance
Given the nature of this 510(k) (modifications to existing systems), the presented "acceptance criteria" are derived from the tests conducted to demonstrate that the changes do not adversely affect safety and effectiveness.
| Category | Specific Test/Feature | Acceptance Criteria (Implied/Stated) | Reported Device Performance/Conclusion |
|---|---|---|---|
| Software | Software Verification and Validation | Compliant with IEC 62304 and FDA Guidance "Content of Premarket Submissions for Device Software Functions" (June 14, 2023) and "Multiple Function Device Products: Policy and Considerations" (July 29, 2020). | Results of executed protocols met the acceptance criteria and therefore support that the system software is acceptable for its intended use. |
| Cybersecurity | Cybersecurity Risk Management | Compliant with FDA guidance "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions'' (September 27, 2023). Appropriate risk mitigation for identified threats and vulnerabilities. | Risk assessment performed, appropriate risk mitigation controls implemented and tested. |
| Bench Testing | Removal of CT/MRI Contraindications, X-ray Caution | Functionality testing acceptance criteria met for sensor performance after exposure to CT, MRI (MR conditional), and X-ray. | The test results showed all functionality testing acceptance criteria was met. (Previous mechanical, electrical, and functional testing established in the predicate device are not affected). |
| App Feature: Auto-Display (FSL2) | Automatic display of glucose results without user-initiated scan. | Functionality: Glucose data automatically displays via BLE without user scan, while NFC scan option remains. | Functionality confirmed. (Users may still perform NFC scan as an option, consistent with predicate FSL2 App behavior.) |
| App Feature: Alarms Escalation | Incremental volume increase for alarms (High, Low, Urgent Low, Signal Loss). | Volume increases incrementally over 30 seconds until maximum volume, equal to predicate device alarm volume. | Functionality confirmed: Alarms increase volume over 30 seconds to the maximum level of predicate device alarms. |
| Core Glucose Monitoring Performance | Clinical Performance (e.g., Accuracy) | Established in predicate device K223435. (No new evaluation specified in this 510(k) summary for these aspects). | Established in the predicate device (K223437) and not affected by the introduction of the FSL App in this 510(k). (This implies that the existing performance data from K223435 is deemed sufficient for these modified devices). |
| Human Factors | Usability and User Interface (new features) | Established in predicate device K223435. (No new evaluation specified in this 510(k) summary for these aspects beyond software V&V). | Established in the predicate device (K223437) and not affected by the introduction of the FSL App in this 510(k). (The app navigation change to "Bottom bar navigation menu" from "Side panel navigation", and the "App Stopped Alarm" for Android were likely part of software V&V and user acceptance testing, implied by overall software V&V acceptance). |
2. Sample Size Used for the Test Set and Data Provenance
The document does not provide specific sample sizes for the detailed technical tests (Software V&V, Cybersecurity, Bench Testing for CT/MRI/X-ray). These types of tests typically involve controlled laboratory environments rather than human subject test sets in the traditional sense for clinical performance.
For the core clinical performance (glucose accuracy, etc.), the document explicitly states these characteristics were "established in the predicate device (K223435) and are not affected." Therefore, any sample size for these fundamental performance aspects would refer to those used in the studies for the predicate device, which are not detailed in this 510(k) summary.
Data Provenance: The document does not specify the country of origin for any testing data. The nature of the tests (Software V&V, Cybersecurity, Bench Testing) suggests they are likely conducted in a controlled environment, potentially in-house or by a certified testing facility. The "retrospective or prospective" nature is not specified, but these are typically prospective tests conducted as part of the development and verification process.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This type of information (number of experts, qualifications, and ground truth establishment) is typically relevant for clinical studies involving expert adjudication (e.g., for diagnostic imaging devices). Given that the 510(k) summary indicates clinical performance was "established in the predicate device" and that the current submission primarily addresses technical and software improvements/modifications, there's no mention of a new clinical study involving multiple experts to establish ground truth for this specific submission.
For the bench testing related to CT/MRI/X-ray compatibility, ground truth would be established by physical measurements and functional checks against engineering specifications, not by human experts.
4. Adjudication Method for the Test Set
Adjudication methods (e.g., 2+1, 3+1) are typically used in clinical studies, particularly for diagnostic accuracy where multiple human readers interpret data. As explained above, this 510(k) focuses on technical and software changes, and refers back to the predicate device for clinical performance. Therefore, no information on an adjudication method for a test set is provided or expected in this context.
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done
No, an MRMC comparative effectiveness study was not done, and it is not typically relevant for a CGM device. MRMC studies are primarily conducted for diagnostic imaging devices to assess how AI assistance impacts human reader performance (e.g., radiologists interpreting scans). CGM devices provide quantitative glucose measurements, and their performance is evaluated directly against a reference method (e.g., laboratory glucose measurements), not through human reader interpretation of complex images or cases.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done
Yes, for the core glucose algorithm, a standalone performance evaluation is inherent to a CGM device. The document explicitly states:
- Glucose Algorithm: "ADC Glucose Algorithm"
- Location of Glucose Algorithm: "FSL2 System: Receiver", "FSL3 System: Sensor"
This implies that the algorithm processes the sensor's raw electrical signals to calculate a glucose value and trend independently. The clinical performance of this algorithm (e.g., MARD against a reference method) would have been part of the studies for the predicate device (K223435). This 510(k) does not present new data for this, but rather relies on the predicate's established performance, stating that ADC (Abbott Diabetes Care) Glucose Algorithm is the "Same" for the subject device.
7. The Type of Ground Truth Used
For Continuous Glucose Monitoring (CGM) devices, the ground truth for establishing accuracy is typically obtained from laboratory reference methods for blood glucose concentration, such as:
- YSI analysis (Yellow Springs Instrument): This is a widely accepted laboratory reference method for measuring glucose in blood or plasma samples.
While not explicitly stated in this 510(k) summary for the ground truth method, it is the standard for CGM accuracy studies which would have been performed for the predicate device to which this submission refers for clinical performance.
8. The Sample Size for the Training Set
The document does not provide a sample size for the training set of the glucose algorithm. This information would typically be detailed in the original predicate device's submission which established the clinical performance. The current 510(k) refers to the "ADC Glucose Algorithm" as being the same as the predicate, indicating no retraining or significant algorithm changes that would necessitate reporting new training data.
9. How the Ground Truth for the Training Set was Established
Similar to the sample size, the specifics of how the ground truth was established for the training set would have been detailed in the original submission for the predicate device. For CGM algorithms, this involves comparing the sensor's raw electrical signals to paired blood glucose values obtained from a highly accurate laboratory reference method (like YSI), collected over time from a cohort of study participants under various glycemic conditions. This establishes the true glucose concentration against which the algorithm learns to convert sensor signals into accurate glucose readings.
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(150 days)
The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.
The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.
The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.
The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.
The FreeStyle Libre 3 Continuous Glucose Monitoring System (hereinafter also referred to as 'FSL3 System') and FreeStyle Libre 2 Flash Glucose Monitoring System (hereinafter also referred to as the 'FSL2 System') are integrated continuous glucose monitoring (iCGM) systems that provide continuous glucose measurements every minute to provide glucose levels, trends, and real-time alarms capability to aid in the management of diabetes. The FSL2 and FSL3 Systems also provide configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss. The user may make treatment decisions based in part on the Sensor glucose results provided by both Systems. The FSL2 and FSL3 Systems require a prescription and are intended for home use.
The FSL3 System consists of the FreeStyle Libre 3 Sensor and either the FreeStyle Libre 3 Reader or the FreeStyle Libre 3 App (iOS and Android) downloaded to a compatible smartphone as a primary display device. The FSL3 Reader and FSL3 App do not interact with each other.
The FSL2 System consists of the FreeStyle Libre 2 Sensor and either the FreeStyle Libre 2 Reader or the FreeStyle Libre 2 App (iOS and Android) downloaded to a compatible smartphone as a primary display device. The FSL2 Reader and FSL2 App do not interact with each other.
Both the FSL2 and FSL3 Systems are compatible with the Libre Data Sharing API cleared in K223537. The display device of the connected FSL2 or FSL3 Systems, which directly receives the data from the Sensor, continues to serve as a primary display device for the glucose data and alarms.
1. A table of acceptance criteria and the reported device performance:
The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed table of clinical acceptance criteria and specific numerical performance. However, it does state that "System accuracy was demonstrated to meet the iCGM special controls requirements per 21 CFR 862.1355." This regulation for Integrated Continuous Glucose Monitoring Systems outlines performance standards.
Based on the document, the following aspects were evaluated:
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Accuracy (Clinical) | Met iCGM special controls requirements per 21 CFR 862.1355. |
| Sterility | Sterility Assurance Level (SAL) of 10^-6 achieved with minimum sterilization dose of 25 kGy, established by VDmax25 method and ISO 11137-1/11137-2. |
| Shelf-Life, Packaging Integrity, Shipping | Seal integrity, user accessibility, and device functionality met acceptance criteria after worst-case testing. |
| Electrical Safety | Compliant with IEC 60601-1: 2005(r)2012, IEC 60601-1-6:2010+A1:2013, and IEC 60601-1-11:2015. |
| Electromagnetic Compatibility (EMC) | Compliant with IEC 60601-1-2 and IEC CISPR 11. Wireless coexistence testing confirmed functionality within acceptable limits in presence of common radiating electronic devices (AAMI TIR69, ANSI C63.27, FDA Guidance). Compliant with FCC Regulations Part 15.225 and Part 15.247, and FAA Advisory Circular RTCA DO-160. |
| Mechanical Engineering | Met acceptance criteria for mechanical, electrical, and functional testing at system level and for individual Sensor Applicator components. |
| Biocompatibility | Demonstrated biocompatibility per ISO10993-1 and FDA Guidance "Use of International Standard ISO 10993-1." |
| Software Verification and Validation | Met acceptance criteria for executed protocols per IEC 62304 and FDA Guidance "Content of Premarket Submissions for Software Contained in Medical Devices." |
| Cybersecurity | Cybersecurity risk management documentation analyzed confidentiality, integrity, and availability of data, information, and software. Appropriate risk mitigation controls implemented and tested per October 2014 FDA Guidance. |
| Human Factors | Risk analysis demonstrated design changes met usability requirements per ANSI/AAMI/IEC 62366, IEC 60601-1-6, and FDA Guidance "Applying Human Factors and Usability Engineering to Medical Devices." |
| Interoperability | Incorporated an approach for interoperability in alignment with FDA Guidance "Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices." |
2. Sample size used for the test set and the data provenance:
The document does not explicitly state the sample size for clinical test sets. For the "Clinical Performance" section, it only mentions that "System accuracy was demonstrated to meet the iCGM special controls requirements per 21 CFR 862.1355." This often implies a clinical trial or study, but specific numbers are not provided in this 510(k) summary. The provenance of the data (country of origin, retrospective/prospective) is also not detailed in this summary.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided in the document. For glucose monitoring systems, "ground truth" is typically established by laboratory reference methods (e.g., YSI glucose analyzer) rather than expert human interpretation.
4. Adjudication method for the test set:
This information is not provided in the document.
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:
This is not applicable to the FreeStyle Libre Continuous Glucose Monitoring System. This device is a standalone diagnostic tool that provides glucose readings directly to the user (or digitally connected devices), not a system designed to assist human readers in interpreting medical images or data. Therefore, an MRMC study comparing human reader improvement with/without AI assistance would not be conducted for this type of device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Yes, the device is inherently designed to operate in a standalone manner (algorithm only) to provide glucose readings. The "Clinical Performance" section implicitly refers to this standalone performance when stating "System accuracy was demonstrated to meet the iCGM special controls requirements per 21 CFR 862.1355." The FSL3 System's glucose algorithm resides in the Sensor, and for FSL2, it resides in the Receiver (App or Reader), generating glucose values without human real-time intervention for each reading.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
For Continuous Glucose Monitoring (CGM) systems, the ground truth for accuracy studies is typically established by laboratory reference methods, such as those using a YSI glucose analyzer, which provides highly accurate and precise blood glucose measurements. While not explicitly stated in this summary, this is standard practice for CGM device validation.
8. The sample size for the training set:
The document does not provide information about the training set size. This 510(k) summary focuses on the substantial equivalence and performance of the final device, not the specifics of its development or algorithm training.
9. How the ground truth for the training set was established:
This information is not provided in the document. Similar to the test set, it would typically involve comparison to highly accurate laboratory reference methods.
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