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510(k) Data Aggregation

    K Number
    K110768
    Date Cleared
    2011-04-20

    (33 days)

    Product Code
    Regulation Number
    872.1800
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    COMPUTED DENTAL RADIOGRAPHY

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

    The Computed Oral Radiology System is indicated for patients undergoing an intra-oral dental x-ray examination. It produces instant, digital, intra-oral images of a patient's mouth while reducing the necessary x-ray dosage.

    Device Description

    The device and its predicates are small digital imaging receptors that may be used in place of dental x-ray film. The images are displayed on a computer workstation. The modified device uses wireless IEEE 802.11 b/g protocol for image data transfer and control signal transfer to and from the Power and Transceiver (PAT) and the host computer. A rechargeable battery power source is also included in the PAT. A Counter Top Dock (CTD) has been added to this system. The CTD is a support device that provides charging power to the PAT and is used to provide a temporary wired connection, via USB, from the host computer to the PAT to allow initial configuration to the host wireless network.

    AI/ML Overview

    This 510(k) summary does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria. The provided document is primarily a summary for a 510(k) clearance, which focuses on demonstrating substantial equivalence to a predicate device rather than detailing extensive performance studies with specific acceptance criteria.

    However, based on the information provided, here's what can be extracted and inferred:

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

    The document does not explicitly state quantitative acceptance criteria or detailed performance metrics. The submission focuses on the modification of an already cleared device (K072134) to incorporate wireless functionality. Therefore, the "acceptance criteria" are mostly implicit in demonstrating that the wireless modification does not degrade the performance or safety of the existing device and maintains substantial equivalence to the predicate.

    Acceptance Criteria (Implied)Reported Device Performance (Implied)
    Functional Equivalence: Image data transfer and control signals function wirelessly.The modified device uses wireless IEEE 802.11 b/g protocol for image data transfer and control signal transfer.
    Image Quality Equivalence: Image quality is maintained despite wireless transfer."The modification does not alter the fundamental technology or the intended use." (Implies image quality is not negatively impacted).
    Safety Equivalence: Wireless operation does not introduce new unacceptable risks."The modified system has had risks evaluated and mitigated as necessary." "Testing and design validation have been used to verify risk mitigation."
    Intended Use Equivalence: Device continues to meet its intended use."The operational environment remains unchanged from the predicate. There are no changes to the indications for use from the predicate devices."
    Predicate Equivalence: Device is substantially equivalent to predicate (K072134)."Schick Technologies has concluded the modified system is substantially equivalent to its predicates. Risk analysis, testing and validation studies support this conclusion."

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

    This information is not provided in the 510(k) summary. The document mentions "Testing and design validation," but does not specify sample sizes, data provenance (e.g., country of origin), or whether these tests were retrospective or prospective.

    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)

    This information is not provided in the 510(k) summary. It's unlikely that such a detailed ground truth establishment would be required or documented in a 510(k) for a modification focused on wireless connectivity, unless there were specific concerns about diagnostic image quality degradation, which are not raised.

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

    This information is not provided in the 510(k) summary.

    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

    This is not applicable to this submission. The device is a digital imaging receptor (X-ray sensor) and associated software for displaying images. It is not an AI-assisted diagnostic tool designed to improve human reader performance.

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

    This is not applicable in the context of an AI algorithm. The device, the Computed Oral Radiology System, is an imaging acquisition and display system. Its primary role is to provide images, not to perform independent diagnoses via an algorithm.

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

    This information is not provided and likely not relevant for a 510(k) focusing on a wireless connectivity modification for an imaging device. The "ground truth" for the performance of such a device generally relates to objective image quality metrics (resolution, signal-to-noise ratio, contrast) and functional performance, rather than diagnostic accuracy against a clinical ground truth.

    8. The sample size for the training set

    This is not applicable in the context of a traditional "training set" for machine learning. The device is not described as involving machine learning or AI that would require a distinct training set. The "training" for this type of device would refer to internal development and testing, not an AI training data set.

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

    This is not applicable as there is no mention of a training set for an AI algorithm.

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