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510(k) Data Aggregation

    K Number
    K103220
    Device Name
    VOYAGER PERITONEAL DIALYSIS SYSTEM
    Manufacturer
    DEKA RESEARCH & DEVELOPMENT CORP.
    Date Cleared
    2011-07-29

    (270 days)

    Product Code
    FKX
    Regulation Number
    876.5630
    Type
    Traditional
    Panel
    Gastroenterology-Urology (GU)
    Reference & Predicate Devices
    K043363,K053512,K012988,K923065
    Why did this record match?
    Reference Devices :

    K043363

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Voyager Peritoneal Dialysis System is intended for automatic control of dialysate solution exchanges in the treatment of adult renal failure patients undergoing peritoneal dialysis.

    All therapies using the Voyager Peritoneal Dialysis System must be prescribed and performed under the responsibility of a physician who is familiar and well informed about peritoneal dialysis.

    Device Description

    The Voyager Peritoneal Dialysis (PD) System is used for automatic control of dialysate solution exchanges in the treatment of adult renal failure patients undergoing peritoneal dialysis therapy. The Voyager Peritoneal Dialysis (PD) System automatically cycles peritoneal dialysis fluid in the amounts and time prescribed by a clinician.

    AI/ML Overview

    This device is a Peritoneal Dialysis (PD) System, not an AI/ML device, therefore most of the requested information (sample sizes, ground truth establishment, MRMC studies, etc.) is not applicable. The document assesses the device's conformance to design specifications and industry standards rather than its performance in an AI/ML context.

    Here's an overview of the acceptance criteria and the study that proves the device meets them, based on the provided text:

    Acceptance Criteria and Device Performance

    Acceptance Criteria CategoryReported Device Performance
    Design SpecificationsMet: The Voyager Peritoneal Dialysis System has been evaluated for conformance to its design specifications. Full system validation and software regression testing were performed to ensure the device functions as intended and that all design requirements have been met.
    Applicable Industry Standards for Software DevelopmentMet: The device has been evaluated for conformance to applicable industry standards for software development.
    System CompatibilityMet: The device is verified for system compatibility with the devices with which it communicates.
    Hardware CertificationMet: Device hardware is certified to applicable safety standards.
    Electromagnetic Compatibility (EMC)Met: EMC testing was conducted according to IEC 60601-1-2. The device was found to meet the requirements defined in that document.
    Electrical SafetyMet: Electrical safety testing was conducted according to IEC 60601-1. The device was found to meet the requirements put forth in that document.
    Human FactorsMet: Human factors testing was performed. The results from the testing demonstrated that all modifications functioned as intended and met pre-determined acceptance criteria.
    Overall FunctionalityMet: Full system validation and software regression testing were performed to ensure that the device functions as intended. Bench testing, simulated use testing, and human factors testing were performed to support substantial equivalence to the predicate device and demonstrate that the device performs as intended and is as safe and effective as the predicate device. The results from the testing demonstrated that all modifications functioned as intended and met pre-determined acceptance criteria. Validation and Verification testing was successful in demonstrating that all design requirements have been met.

    Study Information (Based on the context of a non-AI medical device submission):

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

      • The document does not specify a "test set" in the context of data for an AI/ML model. Instead, the testing described refers to verification and validation activities for a physical medical device and its software.
      • The provenance of data (e.g., country of origin, retrospective/prospective) is not applicable or provided, as the studies are focused on product-based engineering assessments (bench testing, simulated use testing, human factors testing, etc.) rather than clinical data analysis for an AI model.

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

      • This information is not applicable as the device is not an AI/ML diagnostic tool requiring expert-established ground truth from a "test set" of cases. The validation relies on engineering and regulatory standards.
      • The document implies that "a physician who is familiar and well informed about peritoneal dialysis" is responsible for prescribing and performing therapies using the device, but this is for clinical use, not for establishing ground truth for a test set.

    3. Adjudication method for the test set:

      • Not applicable, as there is no "test set" in the context of an AI/ML model for which adjudication would be needed.

    4. 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. This is not an AI-assisted diagnostic or decision-support tool. Therefore, MRMC studies evaluating improvement in human readers with AI assistance were not performed.

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

      • Not applicable, as this is a physical medical device with integrated software, not a standalone AI algorithm. The performance evaluation is of the complete system ("algorithm only" performance is not a relevant concept for this device type).

    6. The type of ground truth used:

      • For this device, "ground truth" is established by adherence to predefined engineering specifications, regulatory standards (e.g., IEC 60601-1-2, IEC 60601-1), functional requirements, and the successful outcome of various performance tests (software validation, EMC, electrical safety, human factors, bench testing, simulated use testing). There is no "pathology" or "outcomes data" in the AI/ML sense for ground truth.

    7. The sample size for the training set:

      • Not applicable. There is no "training set" as this is not an AI/ML device that learns from data. The software is developed and validated, not trained.

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

      • Not applicable, as there is no "training set" for this type of device.
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