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RIGHTEST Blood Glucose Monitoring System Max Tel is intended to the quantitative measurement of glucose (sugar) in fresh capillary whole drawn from the fingertips, forearm, or palm. It is intended to be used by a single person and should not be shared.

RIGHTEST Blood Glucose Monitoring System Max Tel is intended for self- testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to montor the effectiveness of diabetes control. It should not be used for the diagnosis of, or screening for diabetes or for neonatal use. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The RIGHTEST Blood Glucose Monitoring System Max Tel is comprised of the RIGHTEST Meter Max Tel and the RIGHTEST Blood Glucose Test Strip Max.

RIGHTEST Blood glucose monitoring System Max Tel consists of the following devices: Blood Glucose Meter, Blood Glucose Test Strip, Control Solution, Lancing Device and Sterile Lancets. The Blood Glucose Meter, Blood Glucose Test Strips, and Lancing Device are manufactured by BIONIME Corporation.

RIGHTEST Blood Glucose Meter Max Tel, when used with the RIGHTEST Blood Glucose Test Strips Max, quantitatively measure glucose in fresh whole blood samples from capillary. The performance of RIGHTEST Blood Glucose Monitoring System Max Tel is verified by the RIGHTEST Control Solution GC700.

The glucose measurement is achieved by using the amperometric detection meth test is based on measurement of electrical current caused by the reaction of the glucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with FAD-glucose dehydrogenase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration in the sample is displayed.

The provided FDA 510(k) summary for the RIGHTEST Blood Glucose Monitoring System Max Tel focuses on demonstrating substantial equivalence to a predicate device, as opposed to providing detailed clinical study results typical of a de novo or PMA submission. Therefore, much of the requested information regarding a comprehensive study proving acceptance criteria for an AI/device for diagnostic purposes (e.g., number of experts, MRMC studies, ground truth establishment for a training set) is not directly present in this document because it is not an AI/Software as a Medical Device (SaMD) submission for a diagnostic algorithm.

This document describes a glucose monitoring system, which is a medical device rather than an AI-powered diagnostic system that typically involves image analysis or complex algorithmic interpretations of patient data for diagnosis. The "Software Safety Analysis" refers to enabling LTE functionality and adjusting the measurement range, alongside cybersecurity considerations, not the performance of a diagnostic AI.

However, I can extract the acceptance criteria and performance as described in the document for this specific device:

Device: RIGHTEST Blood Glucose Monitoring System Max Tel
Intended Use: Quantitative measurement of glucose (sugar) in fresh capillary whole blood samples for self-testing by people with diabetes at home, as an aid to monitor the effectiveness of diabetes control.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the "Discussion of Non-Clinical Tests Performed for Determination of Substantiability" (Section 8) and the "Comparison to Predicate Devices" (Section 7), the acceptance criteria are generally implied by the successful completion and compliance with relevant FDA guidelines for glucose monitoring systems. The performance is reported in terms of demonstrating compliance.

Overall Conclusion: "Results of performance evaluation of RIGHTEST Blood Glucose Monitoring System Max Tel that had no impacts to BGM measurement was conducted to support substantially equivalent to the predicate device..." |
| Measurement Range: Correct display of "Hi" or "Lo" for out-of-range results. | Hi Lo Display: "The measurement range has been adjusted, and the system displayed a notification indicating 'Hi' or 'Lo'—for results that fall outside the established range." The specific numerical range is 20 - 600 mg/dL (1.1 - 33.3 mmol/L). |
| Software Functionality and Safety:

• Successful implementation and validation of LTE functionality.
• Compliance with FCC testing.
• Compliance with FDA's cybersecurity guidance. | Software Safety Analysis: "Software adjustments were made to enable LTE functionality and adjusted the measurement range. The LTE function was validated through both FCC compliance testing and laboratory testing. As LTE functionality introduced cybersecurity considerations, we ensured compliance with the FDA's guidance on the Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." |
  | Interference: Performance maintained in the presence of specified interferents. | Interference Data Points: Ascorbic Acid ≥ 3 mg/dL, Conjugated Bilirubin ≥ 30 mg/dL, Uric Acid ≥ 12 mg/dL, Xylose ≥ 8 mg/dL. (Implies performance within specification despite these levels, though the exact outcome of the testing is not described beyond listing the tested interferents) |
  | Other Functional Parameters: Measurement technology, sample type, minimum sample volume, test time, control solution compatibility, operating conditions, storage conditions, shelf life, reagent composition, power saving, coding, monitor, backlight, color, power supply, memory capacity, meter dimension, LCD display area, meter weight, data transmission. | All these parameters are listed as characteristics of the new device, implicitly meeting the predicate device's standards or being deemed acceptable (e.g., LTE network for data transmission is a new feature). |
  | General Acceptance: All laboratory studies met acceptance criteria. | "All laboratory studies that the acceptance criteria were met. Therefore, the performances from these laboratory studies were acceptable." |

Regarding the other requested points (relevant for AI/SaMD):

• 2. Sample sized used for the test set and the data provenance: Not specified in the provided document. The reference to "natural and modified blood samples" in "The Extreme Glucose Study" suggests lab-based testing, but no specific sample size or provenance is given.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is not an AI/diagnostic imaging device requiring expert ground truth for interpretation. Ground truth for a glucose meter is typically established by laboratory reference methods (e.g., YSI analyzer).
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This type of adjudication is usually for subjective interpretations by multiple human readers, not for a highly objective measurement device like a glucose meter.
• 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 applicable. This is not an AI system assisting human readers.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device itself is a "standalone" measurement device. Its performance is measured directly against laboratory reference standards, but there is no "algorithm only" in the sense of an AI interpreting complex data that a human would usually interpret.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For glucose meters, the ground truth is typically a laboratory reference method (e.g., a YSI analyzer), rather than expert consensus or pathology, as the measurement is quantitative. This is implied by the nature of the device, although not explicitly stated as "YSI" in the document.
• 8. The sample size for the training set: Not applicable. This device does not use machine learning with a distinct training set in the typical sense of an AI/ML algorithm. Its functionality is based on established electrochemical principles, not pattern recognition learned from a dataset.
• 9. How the ground truth for the training set was established: Not applicable, for the same reason as point 8.

In summary, the provided document is a 510(k) summary for a blood glucose monitoring system, emphasizing its substantial equivalence to a predicate device and compliance with general FDA guidelines for such devices. It does not contain the detailed study results and AI-specific ground truth methodologies that would be found in a submission for an AI-powered diagnostic device.

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RIGHTEST Blood Glucose Monitoring System Max Tel is intended to the quantitative measurement of glucose (sugar) in fresh capillary whole drawn from the fingertips, forearm, or palm. It is intended to be used by a single person and should not be shared.

RIGHTEST Blood Glucose Monitoring System Max Tel is intended for self- testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to montor the effectiveness of diabetes control. It should not be used for the diagnosis of, or screening for diabetes or for neonatal use. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The RIGHTEST Blood Glucose Monitoring System Max Tel is comprised of the RIGHTEST Meter Max Tel and the RIGHTEST Blood Glucose Test Strip Max.

RIGHTEST Blood glucose monitoring System Max Tel consists of the following devices: Blood Glucose Meter, Blood Glucose Test Strip, Control Solution, Lancing Device and Sterile Lancets. The RIGHTEST Blood Glucose Test Strip Max is the same as Test Strip Max cleared in K173638.The Blood Glucose Meter, Blood Glucose Test Strips, and Lancing Device are manufactured by BIONIME Corporation.

RIGHTEST Blood Glucose Meter Max Tel, when used with the RIGHTEST Blood Glucose Test Strips Max, quantitatively measure glucose in fresh whole blood samples from capillary. The performance of RIGHTEST Blood Glucose Monitoring System Max Tel is verified by the RIGHTEST Control Solution GC700.

The glucose measurement is achieved by using the amperometric detection method. The test is based on measurement of electrical current caused by the reaction of the glucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with FAD-glucose dehydrogenase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration in the sample is displayed.

The provided document pertains to the 510(k) premarket notification for the "RIGHTEST Blood Glucose Monitoring System Max Tel." It primarily focuses on demonstrating substantial equivalence to a predicate device through various non-clinical and clinical tests, particularly outlining the system's accuracy.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

The document presents system accuracy results, which serve as the primary performance metrics for the clinical study. The acceptance criteria are implicitly based on the FDA SMBG OTC guidance 2020 (mentioned in the interference section but generally applicable to system accuracy for OTC glucose meters), which typically specifies percentages of results within certain accuracy ranges compared to a reference method.

Table of Acceptance Criteria and Reported Device Performance:

Note: For glucose meters, the acceptance criteria often involve percentages of results within +/-15 mg/dL for glucose concentrations = 100 mg/dL. The document simplifies this to overall percentages within +/-15%, +/-10%, and +/-5% of a reference bias, and mentions 100% within +/-20%. This implies meeting the standard accuracy requirements for blood glucose meters.

Study Details: User Performance Study

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

• Sample Size: 370 participants for the User Performance Study.
• Data Provenance: The document does not explicitly state the country of origin or whether the study was retrospective or prospective. However, user performance studies for regulatory submissions are typically prospective clinical trials. Given the manufacturer (Bionime Corporation) is based in Taiwan and the regulatory consultant is in the US, it's possible the study was conducted in Taiwan or the US, or both.

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

• The document describes a "User Performance Study" where lay users measure glucose. The "System accuracy" section compares these results to a "reference bias" or "comparison method."
• No information is provided about experts establishing ground truth in the way one might for an AI-powered diagnostic image analysis system requiring expert annotation. For a blood glucose monitoring system, the "ground truth" or reference method is typically established by laboratory-grade glucose analyzers, often using a method like hexokinase or glucose oxidase with a highly accurate spectrophotometer. These are standardized laboratory procedures, not dependent on expert interpretation. The document mentions "reference bias," further suggesting a comparison to a precise laboratory method.

3. Adjudication Method for the Test Set:

• Not applicable in the context of a blood glucose monitoring system's accuracy study. Adjudication typically refers to resolving discrepancies among human readers or between human readers and an AI output in diagnostic imaging studies. Here, the comparison is between the device's reading and a precise laboratory reference method.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC study was not done. This type of study is relevant for diagnostic imaging systems where multiple human readers assess cases with and without AI assistance to determine the AI's impact on human performance. For a blood glucose meter, the evaluation is direct device performance against a reference standard, not an improvement in human reader performance.

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

• This concept isn't directly applicable in the same way it would be for an AI algorithm. The device itself (the meter and strips) is the "standalone" entity that produces a measurement. The "User Performance Study" assesses the "human-in-the-loop" aspect by having lay users operate the device and measure their own blood samples. The system accuracy results directly report the device's performance as used by humans.

6. The Type of Ground Truth Used:

• The ground truth (or reference method) for this blood glucose monitoring system study is implied to be highly accurate laboratory-based glucose measurements, against which the device's readings are compared. The term "reference bias" supports this. For blood glucose meters, this reference is typically a carefully calibrated laboratory instrument, not expert consensus or pathology, which are common for AI-based image analysis.

7. The Sample Size for the Training Set:

• Not applicable. This document describes the validation of a blood glucose monitoring system, not an AI or machine learning model that requires a "training set." The system's underlying technology is an electrochemical sensor, not a learned algorithm in the AI sense.

8. How the Ground Truth for the Training Set was established:

• Not applicable. As no training set for an AI model is mentioned, there's no ground truth establishment for a training set.

In summary, the document details a traditional validation approach for a medical device (blood glucose monitor) focusing on its accuracy and performance under various conditions, including lay user operation. It does not involve the complex AI-specific testing methodologies (such as MRMC, training sets, or expert adjudication for ground truth) that would be pertinent to AI/ML-driven diagnostic devices.

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