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510(k) Data Aggregation

    K Number
    K070596
    Manufacturer
    Date Cleared
    2007-06-21

    (111 days)

    Product Code
    Regulation Number
    868.5170
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    TOPICAL APPLICATOR

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

    Intended for atomizing topical anesthetics to the oropharynx and upper airway regions

    Device Description

    The Topical applicator incorporates a small tube with atomizing tip which can connect to a syringe and when placed in the patient's mouth or throat and / or inside an endotracheal tube or in the nasal passages it then delivers topical anesthetic solutions.

    AI/ML Overview

    The provided document is a 510(k) Premarket Notification for a "Topical Applicator" (K070596), comparing it to a predicate device, the Wolfe Tory MADgic (K002255). The acceptance criteria and supporting study are focused on demonstrating substantial equivalence, not necessarily on a novel performance claim beyond the predicate.

    Here's an analysis of the acceptance criteria and the study as described:

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document doesn't explicitly state "acceptance criteria" in a typical numerical pass/fail format for the proposed device's performance alone. Instead, it presents a comparative analysis with a predicate device to demonstrate "substantial equivalence." The implicit acceptance criterion is that the proposed device's performance parameters should be "substantially equivalent" to (i.e., not significantly different from) the predicate device.

    Feature / Performance MetricPredicate Device (Wolfe Tory MADgic K002255)Proposed Device (Topical Applicator)Discussion/Acceptance (Implicit)
    Total Dose Delivered (max 3 cc)
    - Lidocaine2.88 cc2.75 ccSubstantially equivalent
    - Saline2.87 cc2.78 ccSubstantially equivalent
    Residual Mass (Ave.)0.12 cc0.25 ccSubstantially equivalent
    Simulated Clinical Dose %
    - Lidocaine94.7%92.3%Substantially equivalent
    - Saline95.0%94.0%Substantially equivalent
    Particle Size
    - Lidocaine470 um835 um"Not substantially different" because larger particles prevent unintended inhalation (alveolar)
    - Saline470 um803 um"Not substantially different" because larger particles prevent unintended inhalation (alveolar)
    MMAD (um)
    - Lidocaine1.87 um1.93 umSubstantially equivalent
    - Saline1.90 um2.00 umSubstantially equivalent
    GSD (um)SimilarSimilarSubstantially equivalent
    Plume GeometryEvaluated using digital image and photographic grid measuring spray distance, velocity, dispersion angle all vs. timeEvaluated using digital image and photographic grid measuring spray distance, velocity, dispersion angle all vs. timeSimilar (as stated in Section 18, though details not provided here)
    Device Dead Space0.12 ml device only0.13 ml device onlySubstantially equivalent
    BiocompatibilityUnknownTested to ISO 10993-1Proposed device has documented biocompatibility, considered an improvement or at least equivalent.
    Malleable WirePresentAbsentNot a performance requirement; K-resin tubing allows holding shape. Considered "safer" or at least not adversely affecting safety/performance.

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

    • Sample Size: The document does not specify the exact sample size for the performance tests (e.g., how many applications were tested to determine total dose, residual mass, particle size, etc.). It only provides average values.
    • Data Provenance: The document does not explicitly state the country of origin. Given it's a submission to the FDA, it's likely the testing was conducted in the US or in a facility compliant with US regulatory standards, but this is not directly stated. The data is retrospective in the sense that it describes tests performed on the device for the purpose of the 510(k) submission. It is not patient data.

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

    • Number of Experts: This information is not applicable and not provided. The ground truth for device performance metrics (e.g., particle size, delivered dose) is established through standardized laboratory testing methods (e.g., Cascade impactor) and direct measurements, not expert review of subjective data.
    • Qualifications of Experts: N/A for this type of device performance testing.

    4. Adjudication Method for the Test Set:

    • Adjudication Method: Not applicable. Performance testing results are objective measurements from laboratory equipment, not subjective assessments requiring adjudication.

    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 diagnostic devices where human readers interpret medical images or data, and AI assistance is evaluated for its impact on reader performance. This device is a topical applicator, not an interpretive diagnostic tool.

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

    • Technically, the performance testing described (e.g., total dose, particle size, MMAD, plume geometry) is standalone performance, as it evaluates the device's physical characteristics and output without a direct human-in-the-loop performance measurement beyond operating the device for the test. However, it's not "algorithm only" as there's no algorithm being tested in the AI sense. It's direct device performance.

    7. The Type of Ground Truth Used:

    • The ground truth for the performance tests relies on direct physical measurements using established laboratory techniques and equipment, such as a Cascade impactor for particle size and MMAD, and digital imaging/photographic grids for plume geometry. It's objective, quantitative data derived from the device's output.

    8. The Sample Size for the Training Set:

    • Not applicable. This device is a physical medical device, not an AI/ML algorithm that requires a training set of data.

    9. 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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