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The Accu-Chek Solo micropump system with interoperable technology is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin. The Accu-Chek Solo micropump system is able to communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, execute, and confirm commands from these devices. The Accu-Chek Solo micropump system is intended for single patient, home use and requires a prescription. The Accu-Chek Solo micropump system is indicated for use in individuals 2 years of age and greater.

The Accu-Chek Solo micropump is a portable programmable insulin pump, which adheres to the patient's skin. The patch is comprised of two connected parts: a disposable reservoir, in which the insulin is stored and a reusable pump, which includes the pumping mechanism and electronic components. The patch is controlled via a connected Device. The Accu-Chek Solo micropump is designed to deliver basal and bolus insulin doses at various rates, volumes and patterns, as prescribed by the user's physician.

I am sorry, but the provided text does not contain the information required to describe the acceptance criteria and the study that proves the device meets the acceptance criteria, as per your detailed request.

The document is a 510(k) premarket notification summary for the Accu-Chek Solo micropump system. It primarily focuses on demonstrating substantial equivalence to a predicate device (Omnipod DASH Insulin Management System). While it lists various non-clinical tests performed (e.g., human factors, biocompatibility, sterility, electrical safety, software, cybersecurity, interoperability, and system reliability/safety testing), it does not provide specific acceptance criteria or detailed results that quantify device performance against those criteria.

Specifically, it lacks the following information crucial for your request:

• A table of acceptance criteria and reported device performance: The document mentions "The Performance testing of the Accu-Chek Solo micropump System demonstrated that the device met all device specifications," but it does not specify what those specifications are beyond broad categories like "Accuracy of Insulin Delivery" with some general percentages (e.g., "±18% or better at 0.1 U/h" for basal rates). It doesn't present a clear table linking acceptance criteria to actual measured performance for various functionalities.
• Sample sizes used for the test set and data provenance: Details on the number of samples/devices tested for the various non-clinical tests are not provided. There is no mention of a "test set" in the context of an AI/ML device or data provenance (e.g., country of origin, retrospective/prospective).
• Number of experts used to establish ground truth & qualifications: This information is completely absent, as the document doesn't describe a study involving expert assessment or ground truth labeling in the context of AI/ML performance.
• Adjudication method for the test set: Not applicable based on the document's content.
• MRMC comparative effectiveness study: The document does not describe any multi-reader multi-case study, nor does it discuss human reader improvement with or without AI assistance. This device is an insulin pump, not an AI-assisted diagnostic tool.
• Standalone (algorithm only) performance: Not applicable, as this is a physical medical device (insulin pump) with software, not a standalone AI algorithm being evaluated for diagnostic or predictive performance.
• Type of ground truth used: Given the device type, ground truth would relate to physiological parameters (e.g., actual insulin delivered, occlusion detection). While some performance metrics are mentioned (e.g., accuracy of insulin delivery, occlusion detection time), the specific "ground truth" methodology for establishing these values in testing is not detailed in the way one would describe for an AI/ML model (e.g., pathology, outcomes data).
• Sample size for the training set: Not applicable. The document discusses software verification and validation testing, but it does not refer to a "training set" in the context of machine learning model development.
• How ground truth for the training set was established: Not applicable.

The document primarily focuses on:

• Substantial equivalence comparison to a predicate device.
• Confirmation of various engineering and regulatory compliance tests (e.g., electrical safety, EMC, software V&V, cybersecurity, human factors, biocompatibility, sterility, insulin compatibility) without providing specific quantitative results for most acceptance criteria.
• A brief mention of a "clinical study" for the insertion process of the cannula, stating its aim was to prove safety, robustness, and pain-freeness, but it does not provide study details (e.g., sample size, specific outcomes, or quantitative results against acceptance criteria).

Therefore, I cannot fulfill your request based on the provided text, as it describes a clearance for an insulin pump, not an AI/ML device requiring the typical performance evaluation metrics you've outlined.

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