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The MallyaD Injection Pen Adapter is indicated for the capture and wireless transmission of compatible reusable and disposable pen injectors.

The Novo Nordisk PDS290 Injection pens compatible for diabetes treatment are:

• insulin degludec molecule (Tresiba U-100 FlexTouch and Tresiba U-200 FlexTouch)

• insulin aspart molecule (Fiasp FlexTouch)

• insulin degludec and liraglutide molecules (Xultophy)

The concerned Mallya Injection Pen Adapter, also called Mallyaº, is a smart sensor composed of a button to be assembled onto a pen injector by covering the injection pen button. It is designed to be mounted on specific Novo Nordisk® PDS290 platform injection pens. A USB cable, necessary to charge the Mallya® device is also provided in the package.

The given text is a 510(k) Premarket Notification from the FDA for a medical device called the "MallyaD injection pen adapter (MallyaD)". It details the device's indications for use, technological characteristics, and a comparison to a predicate device. While it mentions various tests and guidelines, it does not contain the specific information required to fully answer your request regarding the acceptance criteria and the study that proves the device meets them in the context of an AI/ML medical device.

The "MallyaD" device, as described, appears to be an adapter that records and transmits dosing information from an injection pen. It's a hardware device with associated software, but there is no explicit mention of it being an AI-powered medical device or using machine learning. The "Software controlled" attribute in the table refers to its internal operation rather than AI/ML algorithms.

Therefore, many of your requested points, which are highly relevant for AI/ML device studies, are not present in this document. For example, there's no mention of:

• A table of acceptance criteria for AI model performance (e.g., sensitivity, specificity, AUC).
• Sample sizes for a test set in the context of an AI model's performance on medical images or other data types.
• Data provenance for an AI test set.
• Number of experts, their qualifications, or adjudication methods for establishing ground truth for an AI model.
• Multi-Reader Multi-Case (MRMC) studies for AI assistance.
• Standalone performance for an AI algorithm.
• Type of ground truth for AI model training or testing (e.g., pathology, outcomes data).
• Sample size or ground truth establishment for an AI training set.

The document primarily focuses on the substantial equivalence to a predicate device based on its intended use, technological characteristics (e.g., BLE communication), and physical attributes, along with general performance tests like dose accuracy, biocompatibility, electrical safety, and cybersecurity. The "Dose accuracy: 99% of the recorded doses match the dialed doses with a margin of error of +/- 1 increment" is a performance claim, but it's for the device's basic function, not an AI/ML model's diagnostic or predictive performance.

In summary, based on the provided text, I cannot describe the acceptance criteria and the study that proves the device meets the acceptance criteria as if it were an AI/ML medical device because the document describes a hardware device with embedded software for data capture and transmission, not an AI/ML algorithm or model for diagnostic or predictive purposes.

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