(168 days)
The U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System is intended for use in the quantitative measurement of glucose in fresh capillary whole blood from the finger and the following alternative sites: the palm, the forearm, the upper-arm, the calf and the thigh. It is intended for use by healthcare professionals and people with diabetes mellitus at home as an aid in monitoring the effectiveness of diabetes control program. It is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.
The alternative site testing in this system can be used only during steady-state blood glucose conditions.
This system contains a speaking functionality which provides step by step instructions to aid visually impaired persons.
U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System consists of a meter and test strips. The system utilizes an electrochemical method-based meter and dry reagent biosensor (test strips) for blood glucose testing. The size of the current is proportional to the amount of glucose present in the sample, providing a quantitative measurement of glucose in fresh whole blood and control solutions.
This 510(k) summary provides limited information regarding the acceptance criteria and study details for the U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System. The document states that the performance data of the U-RIGHT TD-4227 system can reference K072784 because "the change between the predicate and proposed devices is only in the brand name." This implies that the current submission (K090188) did not include new performance studies. Therefore, the following answers are based on the assumption that the predicate device (Clever Chek TD-4227 Blood Glucose Monitoring System, K072784) met the necessary criteria, and its performance data would be the basis for demonstrating equivalence. However, the provided document does not contain the actual performance data or study details for the predicate device, K072784.
Based on the provided text, a comprehensive answer for a new study is not possible. However, I can extract the available information and highlight what is missing.
1. Table of Acceptance Criteria and Reported Device Performance
The provided document (K090188) for the U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System does not include a table of acceptance criteria or reported device performance. It explicitly states: "Since the change between the predicate and proposed devices is only in the brand name, the performance data of U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System can be reference to K072784."
To provide this table, one would need to refer to the 510(k) submission for the predicate device, K072784, which is not included in the provided text. Typically, for blood glucose meters, acceptance criteria would align with regulatory standards like ISO 15197, which specify accuracy requirements (e.g., within ±15% or ±20% for certain glucose ranges, or percentage of readings within specific zones of an error grid analysis).
2. Sample Size Used for the Test Set and Data Provenance
The provided document does not specify the sample size used for the test set or the data provenance for the U-RIGHT TD-4227 system, as it references the predicate device (K072784) for performance data.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The provided document does not specify the number of experts or their qualifications for establishing ground truth, as it references the predicate device (K072784) for performance data.
4. Adjudication Method for the Test Set
The provided document does not specify an adjudication method for the test set, as it references the predicate device (K072784) for performance data.
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 question is not applicable to the U-RIGHT TD-4227 No Coding Blood Glucose Monitoring System. This device is a blood glucose meter, an in-vitro diagnostic device that provides a quantitative measurement of glucose. It is not an AI-assisted diagnostic imaging or interpretation device that would involve human readers and MRMC studies.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
This question is not applicable in the context of "algorithm only" as typically understood for AI/ML devices. The device itself is a standalone system for measuring blood glucose from a physical sample. The performance study for such a device assesses its accuracy in measuring glucose compared to a reference method, not an algorithm's classification performance.
7. The Type of Ground Truth Used
Assuming studies for the predicate device (K072784) followed standard practices for blood glucose meters, the ground truth would have been established using a reference laboratory method (e.g., YSI 2300 STAT Plus Glucose & Lactate Analyzer, hexokinase method) on the same blood samples tested by the device. This is a highly accurate chemical analysis, often considered equivalent to "pathology" or gold-standard laboratory results, rather than expert consensus or outcomes data in this context.
8. The Sample Size for the Training Set
The provided document does not specify the sample size for a training set. For a blood glucose meter, it's unlikely there's a "training set" in the machine learning sense. The device's calibration and algorithm are typically established during its development phase using a large number of samples, but this is usually referred to as calibration or development data rather than a "training set" for a distinct algorithm that undergoes continuous learning.
9. How the Ground Truth for the Training Set Was Established
As explained above, the concept of a "training set" in the machine learning context is generally not directly applicable to a traditional electrochemical blood glucose meter. The ground truth for developing and calibrating such a device would be established by comparing its readings to a reference laboratory method across a wide range of glucose concentrations during the design and engineering phases.
Summary of what is present and what is missing from the provided text:
- Present: Device description, intended use, comparison to predicate, a statement that performance data references K072784 due to brand name change.
- Missing from this document:
- Specific acceptance criteria.
- Detailed performance results (accuracy, precision, linearity).
- Sample size of the test set.
- Data provenance (country of origin, retrospective/prospective).
- Details on experts for ground truth.
- Adjudication method.
- MRMC study information.
- Standalone algorithm performance (as typically asked for AI/ML).
- Sample size for a training set.
- Method for establishing ground truth for a training set.
To obtain the specific details requested, one would need to access the 510(k) submission for the predicate device, K072784.
§ 862.1345 Glucose test system.
(a)
Identification. A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.(b)
Classification. Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.