(66 days)
The ARKRAY Diabetes Management Software is an optional accessory for use with compatible blood glucose meters, such as ARKRAY Glucocard Vital Blood Glucose Meter with data management capabilities. The ARKRAY Diabetes Management Software transfers data from the meter's memory into a secured sever for enhanced data management. ARKRAY ARK Care™ Diabetes Management System is intended for use in home and clinical settings via the internet to assist people with diabetes and their healthcare professionals in uploading, storing, analyzing, and communicating about historical blood glucose test results and other biological statistics to support diabetes management.
ARK Care system serves as an interface between the software in personal glucose monitoring devices and a general purpose health management database to assist in the review, analysis and evaluation of blood glucose test results. The secure system is compliant with HIPAA and HITECH standards.
ARK Care is designed for home use and professional healthcare settings. It is an accessory device to most manufactured model home-use blood glucose monitors, including glucose monitoring devices by ARKRAY, Roche, Bayer, LifeScan, and Abbott. The list of supported devices is located at www.arkcare.net.
The purpose of the electronic diabetes management system is to help users and healthcare teams manage blood glucose information to better regulate diabetes treatments and control blood glucose for better health outcomes. The ARK Care system holds a convenience function, as the user can upload blood glucose data from a variety of currently marketed blood glucose monitors into one location for viewing. After the user transmits and stores blood glucose data to the secure database, the ARK Care system allows family members and/or healthcare professionals to view and monitor the user's data and reports. Family and healthcare team members must receive permission from the primary user and create a password protected login before viewing data. ARK Care provides a safe communication portal for the user, family, and healthcare team to send email-like entries, improving patient care and disease management.
The user and approved healthcare team members can view the blood glucose data in different formats such as logbooks, charts, and graphs. The data can be viewed through selected time intervals and these intervals can be compared over time to track disease management.
The subject can also enter and track other health-related information such as body weight, blood pressure, lab values, and exercise activities. Groups of profiles can be queried for bulk reporting on data related to tracking and trending of outcomes, supporting diabetes disease management in individuals and in managed care organizations.
The provided document is a 510(k) summary for the ARKRAY ARK Care Diabetes Management System. It primarily focuses on the device description, intended use, and substantial equivalence determination based on functional and safety testing and consumer studies. However, it does not contain detailed information about specific acceptance criteria, reported device performance metrics in a tabular format, sample sizes for test sets, data provenance, ground truth establishment methods, or the qualifications of experts that would be expected for a rigorous study proving device performance against acceptance criteria for a diagnostic AI system.
Therefore, I cannot extract the full range of information requested in your prompt based on the provided text. I will address what is available and indicate when information is missing.
Here's an analysis of the available information:
1. A table of acceptance criteria and the reported device performance
This information is not explicitly provided in the document in a tabular format with specific criteria and corresponding performance metrics for a diagnostic AI system. The document mentions "Functional and Safety Testing" and "consumer studies that demonstrated the systems ability to be easily operated by in-home users," but it does not detail specific acceptance criteria or quantitative performance results related to the system's analytical capabilities (e.g., accuracy, sensitivity, specificity for analyzing glucose data patterns or predicting outcomes).
The conclusion states: "Labeling, validation testing results and consumer studies results support the Indications for Use and the claim of substantial equivalence to the predicate (K073699)." This indicates that some form of testing was done to demonstrate the system met its intended purpose and was comparable to the predicate, but the specific performance results against pre-defined acceptance criteria are not elaborated.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The document mentions "consumer studies" and "validation testing results," but it does not specify the sample size for these studies or the provenance of the data used (e.g., country of origin, retrospective or prospective nature).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided. The document describes the system as one to "assist people with diabetes and their healthcare professionals in uploading, storing, analyzing, and communicating about historical blood glucose test results and other biological statistics to support diabetes management." It's a data management and communication system, not a diagnostic AI that generates interpretations requiring expert ground truth in the traditional sense. Therefore, the concept of "ground truth" and expert adjudication for a test set, as might be applied to image analysis or diagnostic algorithms, doesn't directly apply here based on the description of the device.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided and is likely not applicable given the nature of the device as a data management system rather than a diagnostic AI that generates interpretations needing expert adjudication.
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
There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study. The device is described as assisting users and healthcare teams in managing blood glucose information. It provides tools for viewing data in different formats and a communication portal. It does not appear to be an "AI" in the diagnostic interpretation sense that would involve human readers making diagnoses with and without AI assistance for a study of this type.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The document describes the "ARK Care system" as an "interface between the software in personal glucose monitoring devices and a general purpose health management database" and a system "to assist in the review, analysis and evaluation of blood glucose test results." This implies a strong human-in-the-loop component for review and decision-making. The concept of "standalone" algorithm performance, as typically applied to an autonomous diagnostic AI, is not applicable here based on the device description. If there are specific analytical algorithms within the system, their standalone performance is not detailed.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not applicable or provided in the context of the device's function as a data management system. For a system that transmits, stores, and presents blood glucose data, the "ground truth" would implicitly be the accurate values reported by the connected blood glucose monitors. The document does not describe any higher-level diagnostic or predictive functions that would necessitate expert consensus, pathology, or outcomes data as ground truth for evaluating its performance.
8. The sample size for the training set
This information is not provided. As the device is described as a data management system and not explicitly as a machine learning/AI model that undergoes a typical training process with labelled data, the concept of a "training set" in that context may not apply or is not detailed.
9. How the ground truth for the training set was established
This information is not provided, and as explained above, the concept of a "training set" and its associated ground truth in the context of an AI model is not evident from the description of this device.
In summary, the provided 510(k) summary for the ARK Care™ Diabetes Management System details its intended use as a data management and communication platform for blood glucose data. However, it lacks the specific technical details, performance metrics, and study designs (e.g., sample sizes, ground truth establishment, expert qualifications, adjudication methods) that would be expected when describing the acceptance criteria and a study proving performance for a diagnostic AI device. The document focuses more on the administrative and regulatory aspects of device clearance, particularly establishing substantial equivalence to a predicate device, and general testing for functionality, safety, and usability.
§ 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.