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510(k) Data Aggregation

    K Number
    K102014
    Date Cleared
    2010-08-31

    (46 days)

    Product Code
    Regulation Number
    882.5890
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    TENS STIMULATOR, MODELS 2800 AND 3000

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

    Symptomatic relief of chronic intractable pain, acute post traumatic pain or acute post surgical pain

    Device Description

    TENS Series Electro-Stimulator, which includes models TENS 2800 and TENS 3000, are Transcutaneous Electrical Nerve Stimulator for pain relief. The stimulator sends gentle electrical current to underlying nerves through the cable and electrode placed on the skin. The parameters of units are controlled by the rotate buttons. Its intensity level is adjustable according to the needs of patients. These TENS electro-stimulator have the same housing in a molded portable plastic case, an accessible, switch, and accessible battery storage compartment. The case shape is rectangular, The process to set the parameter and attach lead wires to the two models is also the same except the Housing printing artwork, Mode No. TENS 3000 stimulator has three treatment mode: normal mode, burst mode and modulation mode. The treatment mode can by selected by switch. TENS 2800 only one treatment mode: normal mode. The difference on the two device can be identified by panel, Mode No.

    AI/ML Overview

    The provided document is a 510(k) summary for a Transcutaneous Electrical Nerve Stimulator (TENS device) seeking substantial equivalence to a predicate device. This type of submission does not typically involve clinical trials with acceptance criteria, human readers, or ground truth as would be seen in studies for AI/ML-based diagnostic or prognostic devices. Instead, it focuses on non-clinical engineering and safety performance.

    Therefore, many of the requested categories are not applicable to this document. I will fill in the relevant information and indicate where information is not applicable.

    1. Table of acceptance criteria and the reported device performance

    Acceptance Criteria (from Predicate Device)Reported Device Performance (TENS 2800/3000)
    Power Source: 9V Battery9V Battery
    Method of Line Current Isolation: Battery Supply N/ABattery Supply N/A
    Patient Leakage Current - Normal Condition: 1.1uA1.2uA
    Patient Leakage Current - Single Fault Condition: 1.3uA1.3uA
    Number of Output Channels: 22
    Method of Channel Isolation: By enclosureBy enclosure
    Regulated Current or Voltage: Voltage controlVoltage control
    Software/Firmware/Microprocessor Control: YesYes
    Automatic Overload Trip: NoNo
    Automatic Over Current Trip: NoNo
    Automatic No Load Trip: NoNo
    Automatic Shut off: NoNo
    Patient Override Control: NoNo
    Indicator Display - On/Off Status: YesYes
    Indicator Display - Voltage/Current Level: YesYes
    Indicator Display - Low Battery: YesYes
    Waveform: Biphasic Rectangular pulseBiphasic or Monophasic Rectangular pulse
    Compliance with Voluntary Standards: IEC60601-1, IEC60601-1-2, IEC60601-2-10IEC60601-1, IEC60601-1-2, IEC60601-2-10
    Compliance with 21 CFR 898: YesYes
    Housing Materials & Construction: Enclosure: ABS,94, V-1,80°C,UL ApprovedEnclosure: ABS,94, V-1,80°C,UL Approved

    Note: Some criteria from the predicate device differ slightly or are not explicitly stated as "acceptance criteria" but serve as a basis for substantial equivalence comparison. For example, timer range, pulse width range, frequency range, weight, and dimensions are compared but aren't presented as strict acceptance criteria in the same way as safety metrics.

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    Not applicable. This is a 510(k) submission for a TENS device, which relies on non-clinical testing (bench testing, safety reports, risk analysis) rather than clinical studies with test sets in the context of diagnostic performance. The document focuses on performance specifications and safety standards.

    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. Ground truth, in the context of expert review, is not relevant for this type of device submission. The "truth" is established by adherence to engineering specifications and safety standards.

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

    Not applicable. No expert adjudication method was used, as this was not a clinical evaluation of diagnostic performance.

    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 is a TENS device, not an AI-assisted diagnostic or prognostic tool. Therefore, MRMC studies and the concept of human reader improvement with AI assistance are not relevant.

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

    Not applicable. This device is a hardware-based TENS stimulator, not an algorithm or AI model.

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

    The "ground truth" for this submission is based on established electrical safety standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-10) and performance specifications that are expected for TENS devices, as demonstrated through bench testing and risk analysis. The comparison to the predicate device's characteristics also serves as a benchmark for substantial equivalence.

    8. The sample size for the training set

    Not applicable. This is an electrical medical device submission, not a machine learning model, so there is no concept of a training set.

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

    Not applicable. As there is no training set for a machine learning model, the concept of establishing ground truth for it does not apply.

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