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The Pump by Babyation is a powered breast pump to be used by lactating women in the hospital or home setting to express and collect milk from their breasts. The Pump by Babyation can be used by multiple users.

The Pump by Babyation is a multiple-user powered breast pump system that allows lactating women to discreetly express and collect milk. It is powered by a rechargeable lithium ion battery. Users have the option of single or double pumping. The device has two phases of pumping, stimulation and expression. Stimulation phase is characterized by faster cycle times and lower suction levels and is used to initiate milk letdown. Expression phase is characterized by slower cycle times and higher suction levels and is used after milk letdown has occurred. The device consists of a main enclosure which houses all of the electrical components that control the system and the pneumatic components that generate suction at vacuum levels up to 250mmHg. The main enclosure also provides an insulated storage area for one ice pack, milk collection bottles, breast shields, and the necessary tubing for the system. The device also includes a mobile app that connects to the device via Bluetooth and allows the user to control the pump.

The provided text describes a 510(k) premarket notification for "The Pump by Babyation," a powered breast pump. While it details non-clinical testing performed to demonstrate device safety and performance, it explicitly states "CLINICAL PERFORMANCE DATA - Not Applicable." This means the submission did not rely on clinical studies for demonstrating substantial equivalence.

Therefore, many of the requested details about acceptance criteria, study design, ground truth, and human reader performance are not present in this document because a clinical study, as typically understood for AI/ML devices or diagnostic accuracy, was not conducted or required for this particular regulatory submission.

However, based on the information provided, I can construct a table for the non-clinical acceptance criteria and summarize the non-clinical tests that "prove" the device meets these criteria.

Here's a breakdown of the information available and what is not applicable (N/A) given the context (a 510(k) for a breast pump, not a diagnostic AI/ML device):

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

Since this is a 510(k) for a physical medical device (a breast pump), the "acceptance criteria" are related to its functional performance, safety, and compliance with general medical device standards, rather than the accuracy of a diagnostic algorithm. The acceptance criteria are implicitly tied to the performance parameters of the predicate device and the relevant standards.

• Vacuum performance: 50-250 mmHg (double/single pumping)
• Speed verification: 30-120 cycles/min
• Milk collection in worst-case scenario
• Battery performance
• Backflow control
• Cross-contamination.
  (Tested using internal test protocols, specific numerical results not provided in this summary but implied to meet internal specifications for substantial equivalence.) |
  | Usability | Usability testing performed per IEC 62366 and IEC 60601-1-6. |

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

• Sample Size for Test Set: Not applicable in the context of a clinical study for a diagnostic device. For the non-clinical bench testing, the "sample size" would refer to the number of devices or components tested. This is not explicitly stated but implies sufficient units to meet testing requirements (e.g., durability, electrical safety tests often involve a small number of units).
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable as this is not a clinical study involving patient data. All testing appears to be lab-based.

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

• N/A. Ground truth as understood for diagnostic accuracy (e.g., radiologist reads) is not relevant here. The "ground truth" for this device's performance would be the specifications and requirements derived from engineering standards and the predicate device's performance.

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

• N/A. Adjudication methods are typically for clinical studies where expert consensus is needed to establish ground truth or resolve discrepancies in human reader performance. This was a non-clinical submission.

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

• N/A. An MRMC study is relevant for evaluating the impact of AI on human reader performance in diagnostic tasks. This device is a breast pump, not an AI diagnostic tool.

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

• While the device has software and a mobile app, it's not an AI algorithm performing a diagnostic task. Its "performance" is mechanical and electronic (suction, cycle speed, battery life, safety features). Bench testing (performance testing, software verification) serves as the "standalone" evaluation of its functional components. The specifics of these tests are outlined in point 1 and 2 above (e.g., vacuum performance, speed verification).

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

• The "ground truth" for this device's performance is established by engineering specifications, industry standards (e.g., ISO, IEC, AAMI), and the performance characteristics of the legally marketed predicate device. For instance, the vacuum range (50-250 mmHg) and cycle speed (30-120 cycles/min) are direct performance metrics that were verified against design specifications.

8. The sample size for the training set

• N/A. This refers to a machine learning context. The "training" for this device would be its design and manufacturing processes, not data training.

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

• N/A. Not applicable as this is not an AI/ML device requiring a training set with established ground truth.

In summary, the provided document is a regulatory submission for a physical medical device (breast pump), not an AI/ML diagnostic or prognostic tool. Therefore, the "acceptance criteria" and "proof" of meeting those criteria are based on extensive non-clinical laboratory testing and compliance with recognized medical device standards, rather than clinical studies involving patient data or human reader performance.

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