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510(k) Data Aggregation

    K Number
    K030907
    Date Cleared
    2003-05-09

    (46 days)

    Product Code
    Regulation Number
    882.1900
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    STACKED ABR FOR NAVIGATOR PRO

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

    The Bio-logic Evoked Potential (EP) product family is indicated for use in the recording and display of human physiological data, for auditory screening purposes and to assist in determining possible auditory and hearing-related disorders. Auditory stimuli are presented to the patient's ear through an earphone or headphones, and the corresponding Auditory Brainstem Responses (ABR) from the patient are recorded using EEG electrodes placed on the scalp. Standard ABR testing is most used clinically for 2 reasons: (1) to predict behavioral audiometric thresholds, and (2) as an audiological testing tool to assist in the assessment of possible auditory nervous system abnormalities.

    Device Description

    The Bio-logic Evoked Potential family of products is intended to be used for the recording and display of human physiological data for auditory testing purposes and to assist in determining possible auditory and hearing-related disorders. The predicate device referenced above is the latest in a series of systems of this type marketed by Bio-logic. The Navigator Pro Evoked Potential Predicate Device performs ABR recording functions with two channels of simultaneous data recording. The software for the Navigator Pro implements the standard Auditory Brainstem Response (ABR) functions common to most similar systems on the market for many years. One of these functions is the testing for auditory nervous system abnormalities but these measures are only effective once the abnormality has sufficiently progressed so as to affect a large number of fibers in the auditory nerve. The Stacked ABR for Navigator Pro device provides software additions to the Predicate Device for the purpose of displaying ABR activity affecting a subset of the nerve. These additional features include automated masking, filtering and data manipulation functions, which allow the observation of ABR test results for frequency band subsets of the nerve fibers in addition to the fibers as a whole.

    AI/ML Overview

    The provided document is a 510(k) summary for the "Stacked ABR for Navigator Pro," a device modification to the Bio-logic Evoked Potential product. It primarily focuses on demonstrating substantial equivalence to a predicate device (Bio-logic Navigator Pro) rather than detailing specific acceptance criteria and a dedicated study to prove device performance against those criteria.

    Therefore, the requested information, specifically regarding detailed acceptance criteria with numerical performance targets, a specific study designed to show the new device meets those criteria, and information like sample sizes for test/training sets, expert qualifications, and ground truth establishment, is generally not present in this type of regulatory submission.

    What is present is a comparison table that highlights similarities and differences between the new device and the predicate device to argue for substantial equivalence. The "Safety and Effectiveness Summary" section describes the processes followed during development to ensure safety and effectiveness, but it does not present a formal study to demonstrate achievement of quantified acceptance criteria for performance.

    Here's an attempt to answer based on the provided text, noting where information is explicitly not available:


    1. Table of acceptance criteria and the reported device performance

    The document does not explicitly state numerical acceptance criteria or quantifiable reported device performance in the manner typically found in a clinical study report for proving performance. Instead, the submission focuses on demonstrating substantial equivalence to an existing predicate device (Bio-logic Navigator Pro) by showing that the new device (Stacked ABR for Navigator Pro) has no significant differences that would adversely affect product safety and effectiveness.

    The comparison table provided in the document focuses on similarities and differences in technological characteristics, not on quantitative performance metrics or acceptance criteria.

    Parameter for comparisonAcceptance Criteria (Not explicitly stated/Likely implicitly "No significant difference to predicate")Reported Device Performance (Implied "No significant difference to predicate")
    Intended UseNo differences to predicate deviceNo differences reported
    PopulationNo differences to predicate deviceNo differences reported
    Hardware ConfigurationNo differences to predicate deviceNo differences reported
    Computer Control SoftwareNew automated features added for derived-band ABRs, time-shifting, alignment, noise-based averaging termination, weighted averaging (no adverse effect on safety/effectiveness)These features were added and deemed to not adversely affect safety/effectiveness.
    Patient information and trackingNo differences to predicate deviceNo differences reported
    Patient connectionsNo differences to predicate deviceNo differences reported
    Presentation of Data / User InterfaceUser Interface essentially same, with added Stacked ABR program options (no adverse effect on safety/effectiveness)User Interface with Stacked ABR options implemented.
    Physical CharacteristicsNo differences to predicate deviceNo differences reported
    Safety CharacteristicsNo differences to predicate device (basic patient connection and isolation circuits are the same)No differences reported
    Product LabelingNo differences to predicate deviceNo differences reported
    Anatomical sitesNo differences to predicate deviceNo differences reported

    Note: The "acceptance criteria" here are implied by the nature of a 510(k) submission, which aims to show "substantial equivalence." The "reported device performance" is essentially the assertion that the device modification successfully implements the described features without negatively impacting safety or effectiveness compared to the predicate.

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

    Not available in the provided document. The submission describes the functionality of the Stacked ABR software and its relation to the predicate device. It does not mention a specific test set, its sample size, or data provenance (e.g., country of origin, retrospective/prospective). This type of detail would typically be found in a separate validation study report, not in a 510(k) summary focused on substantial equivalence for a software modification.

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

    Not available in the provided document. No test set or ground truth establishment process with experts is described.

    4. Adjudication method for the test set

    Not available in the provided document. No test set or adjudication method is described.

    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 and not available in the provided document. This device is a software modification for an Evoked Potential system, primarily automating data manipulation, masking, filtering, and averaging for auditory testing (ABR). It is not an AI-assisted diagnostic tool that would typically involve human readers interpreting images or data with and without AI assistance in an MRMC study. The software "does not make any final decisions that result in any automatic forms of diagnosis or treatment." Its purpose is to provide "additional functionality" and present "a new overall ABR more representative of the patient's hearing across all frequency bands," which is then reviewed by a "qualified user."

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

    Not applicable and not available in the provided document for performance evaluation in the typical sense. The device is a "software addition" to an existing system, and it is explicitly stated that "All program 'recommendations' are subject to review by the EP Technologist or Physician, and may be modified, overridden or deleted as determined by a qualified user." Therefore, it is designed as a human-in-the-loop system, and a standalone performance evaluation in the absence of human input would not be relevant in the context of diagnostic decision-making. The "standalone" aspect would relate to the software's ability to perform its defined data manipulation steps correctly, which is assumed to be part of the internal product development and verification processes mentioned (ISO-9001, ISO-13485, FDA QSR Design Control).

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

    Not applicable and not available in the provided document. Since no explicit performance study with a test set generating a "ground truth" for diagnostic accuracy or similar metrics is described, no type of ground truth is mentioned. The device's "effectiveness" is linked to its ability to generate a "Stacked ABR" that "equally reflects the contributions of all frequency regions of the auditory nerve" and provides "a new overall ABR more representative of the patient's hearing across all frequency bands." This is based on established audiological techniques (derived band technique) and is a data processing function, not a direct diagnostic output that requires a "ground truth" in the same way an AI diagnostic algorithm might.

    8. The sample size for the training set

    Not available in the provided document. The software implements established algorithms (derived-band technique, noise estimation for averaging termination). It is not a machine learning model that requires a "training set" in the common sense for model development.

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

    Not applicable and not available in the provided document. As it's not a machine learning model requiring a training set, the concept of establishing ground truth for a training set does not apply here.

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