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510(k) Data Aggregation

    K Number
    K172040
    Date Cleared
    2018-02-05

    (215 days)

    Product Code
    Regulation Number
    884.1700
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Aquilex Fluid Control System AQL-100S

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

    The Aquilex® Fluid Control System AQL-100S is intended to provide fluid distension of the uterus during diagnostic and operative hysteroscopy and to monitor the volume differential between the irrigation fluid flowing into and out of the uterus.

    Device Description

    The Aquilex® Fluid Control System AQL-100S is a modified version of the primary predicate device, Aquilex Fluid Control System H112 (K112642). The proposed device is a microprocessor-controlled device that consists of the following two main components: (1) an irrigation pump unit including suction pumps (AQL-110P) and (2) and fluid monitoring unit (AQL-100CBS) that are to be placed on a roller stand. The irrigation pump unit (AQL-110P) of the Aquilex® Fluid Control System AQL-100S is a microprocessor-controlled device that functions according to the peristaltic principle and consists of the following components: (1) a casing. (2) a power switch, (3) a power supply, (4) mains cable, (5) a roller wheel, (6) a pump head, (7) suction pumps, (8) various setting keys and (9) display elements. The irrigation pump unit (AQL-110P) is to be used with specially designed single use irrigation and outflow tube sets that are delivered sterile (AQL-110 and AQL-111). In addition, the suction pumps of the irrigation pump unit are to be used with specially designed non-sterile vacuum tube sets (AQL-114). The fluid monitoring unit (AQL-100CBS) consists of the following main components: (1) two scale units, (2), a bag holder, (4) a bag deflector, (5) a container holder, and (6) a roller wheel base. The irrigation pump unit of the proposed device is only operational in conjunction with the fluid monitoring unit.

    AI/ML Overview

    The provided document in the prompt relates to a 510(k) premarket notification for a medical device called the "Aquilex® Fluid Control System AQL-100S." This document describes the device, its intended use, comparison to predicate devices, and a summary of performance data collected to demonstrate substantial equivalence.

    However, the document does not contain specific acceptance criteria tables nor detailed results from a clinical study proving the device meets those criteria. Instead, it provides a summary of various bench tests and compliance with standards (electrical safety, EMC, software, biocompatibility, sterilization, shelf life) to support its substantial equivalence claim, rather than a clinical study with acceptance criteria for device performance in a real-world or simulated clinical setting.

    Therefore, many of the requested details about acceptance criteria, study design, sample sizes, expert involvement, and ground truth cannot be extracted directly from this document in the manner typically associated with studies demonstrating performance against specific clinical or diagnostic metrics.

    Based on the available information, here's what can be inferred and what cannot:

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

    The document mentions "Bench testing was performed to demonstrate that the fluid deficit determination...is substantially equivalent...in terms of accuracy." However, it does not provide a specific table of acceptance criteria for this accuracy or the reported numerical performance values (e.g., mean accuracy, standard deviation, or specific thresholds).

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

    • Test Set Sample Size: Not specified for any of the performance tests. The bench testing refers to "fluid deficit determination" but doesn't quantify the number of measurements or conditions.
    • Data Provenance: The tests (electrical safety, EMC, software verification/validation, biocompatibility, sterilization, shelf life, bench testing) are described as performed by "independent laboratories" or internally. No information on country of origin of data or whether it was retrospective or prospective is given, as these are typically laboratory/bench tests, not clinical studies.

    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):

    Not applicable. The performance tests described are laboratory-based and do not involve human interpretation or subjective assessment that would require "experts to establish ground truth" in the clinical sense.

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

    Not applicable. There's no human adjudication involved in the described bench and standard compliance tests.

    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:

    Not applicable. This device is a fluid control system, not an AI or imaging diagnostic device that would involve human readers or MRMC studies.

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

    The performance tests are essentially standalone for the device, as they assess its functional capabilities (e.g., electrical safety, fluid deficit accuracy) without human intervention in the primary measurement. However, this is not an "algorithm only" study in the context of AI, but rather a functional characterization of hardware and software.

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

    For the bench testing related to "fluid deficit determination," the ground truth would likely be established by highly accurate reference measurement systems (e.g., precise scales or flow meters) that are calibrated and traceable to known standards. This is inherent in laboratory bench testing. The document doesn't explicitly state the specifics of this ground truth.

    8. The sample size for the training set:

    Not applicable. This is not an AI-driven device requiring a training set in the typical machine learning sense. The software development and testing, while mentioned (IEC 62304), refers to traditional software verification and validation, not machine learning model training.

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

    Not applicable, as there is no training set for an AI model.


    Summary of available information:

    CriterionDetails from Document
    Acceptance Criteria & Reported PerformanceAcceptance Criteria: Unspecified. The document states bench testing was performed to demonstrate "fluid deficit determination...is substantially equivalent...in terms of accuracy" to the predicate device (Hysteroscopy Pump HM6). No numerical acceptance thresholds or target accuracy values are provided.

    Reported Performance: No specific numerical performance values (e.g., accuracy percentages, error ranges) are reported for the fluid deficit determination. The conclusion is that the device "is substantially equivalent" to the predicate for this function. |
    | Test Set Sample Size | Not specified for any of the performance tests. |
    | Data Provenance | Tests performed by "independent laboratories" or internally. No details on country of origin or retrospective/prospective nature. |
    | Number & Qualifications of Experts for Ground Truth | Not applicable; tests are laboratory-based, not reliant on expert clinical interpretation for ground truth. |
    | Adjudication Method for Test Set | Not applicable; no human adjudication involved in these functional tests. |
    | MRMC Comparative Effectiveness Study | No. This is a fluid control system, not an imaging/AI diagnostic device. |
    | Standalone Performance Study | Yes, the various bench, electrical safety, EMC, software, biocompatibility, sterilization, and shelf-life tests assess the device's performance in a standalone context against relevant standards and predicate device functions. |
    | Type of Ground Truth Used | For "fluid deficit determination," ground truth would be established by precise, calibrated reference measurement systems in a laboratory setting. Not explicitly detailed in the document. |
    | Training Set Sample Size | Not applicable; this device does not use machine learning that requires a training set. Software verification and validation followed IEC 62304. |
    | How Training Set Ground Truth was Established | Not applicable; no training set for AI. |

    The document primarily relies on demonstrating compliance with recognized standards (IEC 60601-1, IEC 60601-1-2, IEC 62304, ISO 10993 series, ISO 11135, ISO 14937, ISO 11607-1, ASTM-F1980) and bench testing indicating functional equivalence to a predicate device for its 510(k) clearance, rather than reporting detailed clinical study results with explicit acceptance criteria often found for diagnostic or treatment efficacy devices.

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