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The Annabella Breast Pump is a powered breast pump to be used by lactating women to express and collect milk from their breasts. The Annabella Breast Pump is intended for a single user.

The Annabella Breast Pump is a single, electric breast pump system intended to express and collect milk from the breasts of lactating women. It is comprised of a vacuum unit including tubing and a massage unit that mimics the baby's sucking motions. The user employs buttons on the vacuum unit to control the vacuum intensity levels, suction rate as well as the massage unit speed and location on the breast. The device is supplied with a reusable, washable and adjustable breast shield, a bottle lids and a charger. The Annabella is a rechargeable Li-ion battery operated device that contains software. The device cannot be operated while connected to the mains AC Power core. The battery compartment is at the bottom of the pump motor unit and is covered by a battery door. The runtime of the removable lithium-ion battery is influenced by the number and duration of pumping sessions and lasts usually for one day. The device is to be used in the home.

The Annabella Breast Pump is a powered breast pump intended for lactating women to express and collect milk. The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device (Medela Freestyle, K150499) rather than presenting a standalone efficacy study with acceptance criteria in the traditional sense of medical device performance for diagnosis or treatment. Therefore, the information typically found for such a study (like effect size for AI assistance, expert qualifications, adjudication methods, ground truth types for test/training sets, and large sample sizes for studies proving efficacy) is not applicable here.

The "acceptance criteria" for this submission are primarily demonstrating safety and performance equivalence to the predicate device through non-clinical testing. The "study" proving this involves various non-clinical performance data and compliance with relevant standards.

Here's a breakdown of the requested information based on the provided text, with explicit notes where information is not applicable or not provided for this type of device and submission:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally implied by adherence to standards and comparison to the predicate. The "performance" is reported as having successfully met these standards and demonstrating functionality similar to the predicate, accounting for differences.

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

• Sample Size for Test Set: Not applicable in the context of a clinical study for diagnostic/treatment efficacy. The "test set" here refers to the actual devices used for non-clinical performance and safety testing. The number of physical units tested is not specified but would typically be a small number of production or representative samples.
• Data Provenance: Not applicable for typical device testing. The tests are conducted in a controlled laboratory environment by the manufacturer or a contracted testing facility. No patient data or geographical origin is relevant for these types of non-clinical tests.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not applicable. Ground truth, in this context, refers to engineering specifications and regulatory standards for safety and performance testing (e.g., electrical safety, EMC, biocompatibility). These are objective measurements against established criteria, not subjective expert interpretations for clinical diagnosis.
• Qualifications of Experts: Not applicable. The "experts" would be qualified engineers and technicians performing the tests according to defined protocols and standards, not clinical radiologists or similar.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Adjudication methods (like 2+1 or 3+1) are used to resolve disagreements in subjective interpretations (e.g., expert reads of medical images). For objective non-clinical performance and safety testing, measurements either meet or do not meet a predefined standard; there's no subjective difference to adjudicate.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study Done?: No. An MRMC study is typically for evaluating the diagnostic performance of a medical imaging device or AI algorithm with human readers. This device is a breast pump, not an imaging device or an AI diagnostic tool.
• Effect Size of Human Readers Improvement with AI: Not applicable, as there is no AI assistance for human readers in the context of a breast pump.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

• Standalone Performance Done?: No, not in the sense of a standalone diagnostic algorithm. The device does contain software, and its performance was verified as part of the software verification testing (IEC 62304). However, this refers to the pump's operational software, not a standalone diagnostic algorithm being evaluated for clinical accuracy.

7. Type of Ground Truth Used

• Type of Ground Truth: For the non-clinical performance and safety tests, the "ground truth" consists of established engineering specifications, recognized national and international safety standards (e.g., IEC, ISO), and regulatory requirements set by agencies like the FDA. For example:
  • Electrical Safety: Standards like IEC 60601-1 define acceptable leakage currents, insulation breakdown, etc.
  • Biocompatibility: ISO 10993 standards define acceptable levels of cytotoxicity, irritation, and sensitization.
  • Device Performance: Internal design specifications for vacuum levels, cycle speeds, and battery life, which are then compared against objective measurements.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a physical medical device, not an AI/ML diagnostic algorithm that requires a "training set" of data in the typical sense. While products undergo design and development, which can be seen as an iterative "training" process for engineering, it's not a data-driven training set like for AI.

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

• How Ground Truth for Training Set Established: Not applicable. As there's no "training set" in the AI/ML context, there's no need to establish ground truth for it. The design and development process for a physical device relies on engineering principles, materials science, user needs, and regulatory requirements rather than training data with established ground truth.

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