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510(k) Data Aggregation

    K Number
    K220974
    Device Name
    R:GEN
    Date Cleared
    2022-12-27

    (267 days)

    Product Code
    Regulation Number
    886.4390
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K122202, K121475, K123542

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

    The R:GEN is indicated for use by an ophthalmic physician to produce a wound to the Retinal Pigmented Epithelium (RPE) of the retina of the eye, via focal laser treatment of Clinically Significant Macular Edema (CSME).

    Device Description

    The R:GEN is a surgical laser system for use by ophthalmic physicians for performing focal laser treatment, also referred to as selective retinal therapy for the treatment of various retinal diseases by wounding Retinal Pigmented Epithelium (RPE) cells. It is the product of continual technological evolution of the R:GEN technology described in the predicate device (K153769). It consists of the following main components: Main Body, LCD Monitor, Slit Lamp Set, Footswitch, and Real Time Feedback (RTF) Contact Lens which consists of the RTF Body (Real Time Feedback Body) and RTF Sensor (Real Time Feedback Sensor). The Main Body allows the proper operation of the entire system. It contains the laser and is attached to the Footswitch and optical fiber. The laser is a Q-switched Nd:YLF (Neodymium-doped Yttrium Lithium Fluoride) laser with an emission wavelength of 527 nm (produced by second-harmonic generation). The integrated software provides all the functions which are necessary to use the device. The Slit Lamp Set contains the laser delivery system. The laser beam emitted through the Slip Lamp Set is transmitted through the RTF Contact Lens (first through the RTF body and then through the RTF Sensor), and finally irradiated to the patient's retina.

    AI/ML Overview

    This document is a 510(k) Summary for the R:GEN ophthalmic laser system. It describes the device, its intended use, and argues for its substantial equivalence to a predicate device (K153769) and several reference devices (K122202, K121475, K123542).

    The information provided does not include explicit acceptance criteria in the format of a table with reported device performance alongside it. Instead, it describes performance data from various types of studies (bench, animal, and clinical evidence) to support the device's safety and effectiveness and its substantial equivalence to the predicate.

    Here's an analysis of the provided text in relation to your questions:

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

    The document does not contain a discrete table of acceptance criteria and reported device performance. It generally states that the device "substantially conforms to the performance standards for light emitting products (21 CFR 1040)" and meets various IEC and ISO standards. It also mentions that animal studies "established that the use of subject device wounds RPE cells" and published clinical performance testing "established that the subject device performs as intended in the treatment of CSME."

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

    • Bench Testing: No specific sample sizes for test sets are mentioned. The testing was performed in accordance with standards (IEC, ISO, ASTM).
    • Animal Testing: No specific sample size is mentioned for the R:GEN animal study. It mentions "Additional published animal studies." Data provenance (country, retrospective/prospective) is not provided.
    • Clinical Evidence: Refers to "Published clinical performance testing," but does not provide specific sample sizes, country of origin, or whether it was 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 is not applicable as the studies described (bench, animal, and "published clinical evidence") do not detail the use of experts for ground truth establishment in a diagnostic-like context. The device is a surgical laser system, so "ground truth" would likely refer to objective outcomes related to RPE wounding or CSME treatment, rather than expert interpretation of images or data to establish a diagnosis.

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

    This is not applicable for the types of studies described (bench, animal, and general clinical performance) as there is no mention of expert consensus or adjudication for a test set.

    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. The R:GEN is an ophthalmic laser system, not a diagnostic AI device that assists human readers.

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

    This is not applicable as the R:GEN is a surgical laser system and not a diagnostic algorithm.

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

    • For the animal studies, the ground truth appears to be direct observation of "wounds to RPE cells."
    • For the clinical evidence, the ground truth is referred to as "performing as intended in the treatment of CSME," which implies clinical outcomes data related to CSME treatment. However, the specific metrics or methods for establishing this ground truth are not detailed.

    8. The sample size for the training set

    This is not applicable. The R:GEN is a hardware device (laser system) with integrated software. The document refers to "Software Verification and Validation," which implies software testing, but there is no mention of a "training set" in the context of machine learning.

    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 machine learning.

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