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The Electric Breast Pumps are intended to express and collect milk from the breast of a lactating woman.

The Electric Breast Pump BLA8015-02 is designed and manufactured to express and collect milk from a lactating woman's breast. The subject device is electrically powered, software-controlled, digital single-user pumps. The pump is available in 4 different modes, which are massage mode, continuous express mode, frequency conversion mode and normal express mode. The different modes can be distinguished by different keys displayed on the LCD. There are 6 expression levels for massage mode and continuous expression mode, and 9 expression levels for frequency conversion mode and normal express mode. The suction strengths are different for different working modes. The suction strength can be adjusted within 20~240mmHg by the user. When powered, the subject device starts with massage mode at level 1. The mode switch and level adjustment can be achieved by mode keys. A suction cycle consists of an inhale process and an exhaust process. When the breast pump is powered on, the vacuum pump exhausts the air inside the cavity. When the suction strength reaches the setting value, the vacuum pump stops working and the expressed milk opens the valve and then flows into the bottle due to pressure difference. The pump is provided in non-sterile and can be re-used by a single user. The device has embedded software and the level of concern for the software is minor. The device is powered by Li-ion battery. The patient contact component and milk contact component are massage cushion and milk bottle. The contact duration and contact level is limited skin contact. The massage cushion and milk bottle are respectively made of polypropylene and silicone gel. The materials meet the requirements of FDA regulations concerning food contact. The device can be used in both hospital and home environment.

This document is a 510(k) Summary for an Electric Breast Pump (K191802). It is a regulatory submission to the FDA seeking clearance for a new medical device by demonstrating its substantial equivalence to a legally marketed predicate device.

Here's an analysis of the provided information regarding acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly present a "table of acceptance criteria" in the format of specific thresholds for performance metrics. Instead, it refers to compliance with recognized standards and design specifications. However, we can infer the acceptance criteria from the performed tests and the comparison to the predicate device.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: The document does not specify a distinct "test set" sample size in the context of clinical data or patient studies. The non-clinical tests conducted (e.g., electrical safety, EMC, biocompatibility, vacuum pressure, backflow) would typically involve a sample of the manufactured device. However, the exact number of devices tested for each non-clinical test is not explicitly stated.
• Data Provenance: The studies are described as "non-clinical tests" conducted to verify design specifications and compliance with standards. These are typically laboratory-based engineering and material tests, not clinical studies involving human subjects. Therefore, there is no mention of country of origin for patient data or whether the data is retrospective or prospective, as it's not applicable here.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

This information is not applicable to this submission. The "ground truth" for the non-clinical tests is established by objective engineering measurements against defined specifications and regulatory standards. There is no mention of human experts establishing ground truth for these device performance tests in the same way experts would establish truth for a diagnostic AI model.

4. Adjudication Method for the Test Set

This information is not applicable. Adjudication methods (like 2+1, 3+1) are typically used in clinical studies or for establishing ground truth in diagnostic accuracy studies where there's variability or disagreement among human readers or expert interpretations. Since the tests described are non-clinical engineering and material tests, such adjudication is not used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance, particularly in diagnostic imaging or similar fields. This submission is for an Electric Breast Pump, where the primary evaluation is about physical performance, safety, and substantial equivalence, not human reader diagnostic improvement.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

The device is an Electric Breast Pump, not an algorithm meant to operate diagnostically in a standalone fashion. While it has "embedded software" of minor concern, the performance studies described are for the integrated device's physical functions (e.g., vacuum pressure, backflow). Therefore, a "standalone (algorithm only)" performance study in the context of AI diagnostics was not done or applicable. The software was verified and validated as part of the overall device.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical performance tests is based on:

• Established engineering specifications and design requirements: For parameters like vacuum pressure and cycle speed.
• Compliance with recognized international standards: Such as IEC 60601-1 for electrical safety, IEC 60601-1-2 for EMC, and ISO 10993 for biocompatibility. These standards define the acceptable limits and test methodologies.
• Absence of undesirable events: For example, "no backflow" for the backflow prevention test, "no cytotoxicity" for the biocompatibility tests.

There is no mention of "expert consensus," "pathology," or "outcomes data" being used as ground truth for these specific non-clinical tests.

8. The Sample Size for the Training Set

This information is not applicable. The device is an Electric Breast Pump, not an AI model that requires a "training set" of data in the conventional sense for machine learning. The device's embedded software is of "minor level of concern," implying it likely controls operational functions rather than performing complex AI-driven tasks that would necessitate a large training dataset.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable for the same reasons as point 8.

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