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The MilkMate Breast Pump is intended to be used by lactating women to express and collect milk from their breast, to alleviate engorgement of the breast, maintain the ability of lactation, and provide mother's milk for future feedings when separation of mother and baby occurs. It is intended for multiple users in places of work, shared spaces, healthcare facilities, hospitals, and the home.

The MilkMate Breast Pump includes a multi-user breast pump and disposable breast pump kits for the convenience of pumping in the workplace, shared spaces, healthcare facilities, hospitals, or the home environment. The breast pump provides two modes: stimulation and expression mode. It can be operated using AC power or a built-in rechargeable li-ion cylindrical battery. The kits are comprised of two each of a breast shield, breast shield body, backflow protector, valve and membrane, standard neck bottle pouch with cap, tubing, and tubing connector. Five breast shield sizes are offered (21 mm, 27 mm, 27 mm, 30 mm, and 36 mm). The kits are pre-assembled and sterile.

The provided text describes a 510(k) premarket notification for the MilkMate Breast Pump, focusing on the substantial equivalence to a predicate device. Here's an analysis of the acceptance criteria and study information, based on the input provided:

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

The document does not explicitly state formal "acceptance criteria" for clinical performance in a quantifiable manner (e.g., specific thresholds for milk production increase or user satisfaction ratings). Instead, the performance testing focused on demonstrating that the new breast shield sizes perform comparably to the predicate device in terms of vacuum and cycle settings.

However, based on the non-clinical performance testing summary, here's what can be inferred as performance metrics evaluated:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

The document does not provide details on the sample size for the test set or the data provenance. The testing appears to be primarily non-clinical performance testing of the device's physical characteristics (vacuum and cycle behavior) rather than real-world user data. Therefore, concepts like country of origin or retrospective/prospective are not directly applicable in the way they would be for clinical studies involving human subjects.

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 applicable to the type of non-clinical performance testing described. Ground truth in this context would be the objective measurements of vacuum and cycle settings, typically established by engineering standards and calibrated equipment. There is no mention of human experts establishing "ground truth" for these technical measurements.

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

This is not applicable as the testing described is non-clinical performance testing, not a study evaluating interpretations or conditions that would require adjudication among human reviewers.

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 is not applicable. The device is a breast pump, not an AI-powered diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human readers or AI assistance would not be relevant.

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

This is not applicable. The device is a physical breast pump, not an algorithm.

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

The "ground truth" for the non-clinical performance testing would be the engineering specifications and objective measurements of vacuum pressure and cycle speed using calibrated instruments. The goal was to confirm that the modified device's performance aligns with these objective specifications and the performance of the predicate device.

8. The sample size for the training set:

This is not applicable. The document describes a physical medical device, not a machine learning algorithm that requires a training set.

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

This is not applicable, as there is no training set for an algorithm.

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