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The Disetronic Multifuse Infusion Pump, with its accessories, is indicated for the controlled delivery of parenteral fluids, including patient controlled analgesia (PCA), in both the hospital and home care environments.

The Disetronic Multifuse pump is a small, battery-operated peristaltic infusion pump that is suited for ambulatory hospital and home use. The pump is extremely versatile. It has been designed to allow the health provider program the pump to provide one of several types of infusion therapies. The pump can be configured as a continuous rate infusion pump with demand bolus capability, a variable rate infusion pump with demand bolus capability, a Patient Controlled Analgesia (PCA) pump, a Total Parenteral Nutrition (TPN) pump or an intermittent infusion pump. The health provider can further customize the pump for a specific patient by allowing or excluding optional programming steps, limiting allowable ranges, adjusting the rate at which boluses are infused and adjusting alarm and display features.

The provided text describes a submission for a Disetronic Multifuse Infusion Pump System (K980259) seeking substantial equivalence to a previously marketed device (K915566). This is a traditional 510(k) summary for a medical device and does not involve clinical studies with acceptance criteria, sample sizes, expert ground truth, or MRMC studies in the way that AI/ML devices typically do.

Therefore, most of the requested information regarding acceptance criteria, study design, expert involvement, and ground truth establishment is not applicable to this type of regulatory submission.

Here's an assessment based on the provided document:

1. A table of acceptance criteria and the reported device performance:

2. Sample size used for the test set and the data provenance:

• Not Applicable. This submission is for a physical medical device (infusion pump), not a data-driven AI/ML device that uses test sets of data. The "testing" refers to functional and safety testing of the hardware and software, not evaluation on a dataset.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. Ground truth, in the context of AI/ML, refers to definitively labeled data. For an infusion pump, the "ground truth" relates to physical performance metrics (e.g., accuracy of fluid delivery, alarm functionality), not interpretations of data by experts. Testing was likely performed by engineers against predefined specifications.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not Applicable. Adjudication methods are relevant for resolving discrepancies in expert interpretations of data (e.g., medical images). This process is not part of the functional and safety testing of an infusion pump.

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. MRMC studies are specifically for evaluating the impact of AI on human reader performance, typically in diagnostic tasks. This device is an infusion pump, which does not involve human readers interpreting data.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

• Not Applicable. While the pump has software that operates "standalone" in its primary function, this question is typically posed for AI algorithms and their diagnostic accuracy independent of human input. The pump's performance is inherently "standalone" in delivering fluids according to its programming.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not Applicable in the traditional sense of AI/ML ground truth. The "ground truth" for this device's testing would be regulatory standards (e.g., IEC 601-2-24), engineering specifications, and validated performance metrics (e.g., flow rate accuracy, pressure limits, alarm thresholds). These are established through engineering design, regulatory requirements, and industry standards, not by expert consensus on data interpretation.

8. The sample size for the training set:

• Not Applicable. This is a hardware/software device, not an AI/ML model that undergoes "training" on a dataset. Software development and testing follow different paradigms.

9. How the ground truth for the training set was established:

• Not Applicable. As there is no training set in the AI/ML sense, there is no ground truth established for it. Software verification and validation are performed against requirements and specifications.

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