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510(k) Data Aggregation

    K Number
    K130158
    Device Name
    M-SCAN
    Date Cleared
    2013-07-25

    (183 days)

    Product Code
    Regulation Number
    890.1375
    Panel
    Dental
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    M-SCAN

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

    Indications for use; 2 channel, hand held, mobile EMG amplifier

    1. To record electrical activity of 2 muscles of the stomatognathic system, especially temporalis or masseter
    2. To clinically monitor 2 different muscles as an aid in the diagnosis and treatment evaluation by recording the electrical activity of muscles of the stomatognathic system
    3. To determine the degree of relaxation (intra-patient) of 2 muscles at rest
    4. To measure relative (intra-patient) levels of activity of 2 muscles during a function act
    Device Description

    The M-Scan is a 2-channel, portable, battery operated electromyographic amplifier which includes: a) two (2) identical high-gain differential input amplifiers, b) two (2) buffer amplifiers, c) two (2) full wave rectifiers, and d) two (2) integrators. The overall amplification of the M-Scan is calibrated to 2500. The bandwidth filtering is set (fixed) from 30 Hz to 1000 Hz (± 3dB). The common mode rejection ratio is ≥ 130 dB at the power line frequency (50/60 Hz). The M-Scan does not include isolated power converter since it is battery operated, portable, and does not interface with any external equipment (it is never connected to the a-c line). Only three (3) functions are provided by the M-Scan: 1) amplification, 2) bandwidth limiting, and 3) integration of the signal for the integrated display.

    AI/ML Overview

    Here's an analysis of the provided text regarding the M-Scan device, focusing on acceptance criteria and study details.

    Important Note: The provided text is a 510(k) summary for a medical device (M-Scan). It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with explicit acceptance criteria for a new clinical claim. Therefore, much of the requested information (like specific acceptance criteria values, sample sizes for deep learning models, expert qualifications, etc.) is not present in this type of document because it's not a clinical effectiveness study. The "study" described here is a non-clinical performance comparison to a legally marketed predicate device.


    Acceptance Criteria and Device Performance

    There are no explicitly stated numerical acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy) in this 510(k) summary, as it's not a clinical performance study aiming to prove a specific diagnostic efficacy. Instead, the acceptance criteria implicitly involve demonstrating that the M-Scan's electrical characteristics are comparable or substantially equivalent to its predicate device (BioEMG III).

    Acceptance Criteria (Implied)Reported Device Performance
    Overall amplification comparable to predicate (2500)"overall amplification of the M-Scan is calibrated to 2500"
    Bandwidth filtering comparable to predicate (30 Hz to 1000 Hz ± 3dB)"bandwidth filtering is set (fixed) from 30 Hz to 1000 Hz (± 3dB)"
    Common mode rejection ratio (CMRR) comparable to predicate (≥ 130 dB)"common mode rejection ratio is ≥ 130 dB at the power line frequency (50/60 Hz)"
    Waveforms and frequency response characteristics comparable to predicate"comparable graphic plots of the waveforms and the frequency response characteristics of both instruments" (in Appendix E, not provided here)
    "retained comparable levels of amplification and band-pass filtering as used in the BioEMG III predicate device."

    Study Details

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

      • Test Set Sample Size: Not applicable in the context of human patient or imaging data. The "test set" consisted of artificial signals generated by a calibrated function generator. The number of such test signals or their variations is not specified.
      • Data Provenance: Not applicable as it's not real-world patient data. The "data" was synthetically generated using a function generator.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • Not applicable. The ground truth for the instrument's electrical performance was established by objective measurements using a calibrated function generator and oscilloscope, not clinical experts.
    3. Adjudication method for the test set:

      • Not applicable. The "test" involved direct electrical measurement and comparison to the predicate device's measured characteristics, not a human reader adjudication process.
    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:

      • No MRMC study was done. This device is an electromyograph (EMG) amplifier, not an AI-powered diagnostic tool interpreting complex data. There is no AI component mentioned that would assist human readers, or any reader component at all other than clinical interpretation of the EMG signals generated.
    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

      • Not applicable. This device is a measurement instrument. Its "performance" is its ability to accurately amplify and filter EMG signals, not to provide a diagnostic interpretation that could be done "standalone." Software validation testing was performed on the device's software (likely firmware controlling components and display logic), but not a standalone 'algorithm' in the sense of AI.
    6. The type of ground truth used:

      • Objective electrical measurements: The ground truth for the M-Scan's performance was established by applying known, calibrated electrical signals from a function generator and measuring the output with an oscilloscope. This allowed for direct comparison of amplification, bandwidth, and waveform characteristics against the predicate device.
    7. The sample size for the training set:

      • Not applicable. This device does not use machine learning or AI that would require a "training set."
    8. How the ground truth for the training set was established:

      • Not applicable, for the same reason as above.
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