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510(k) Data Aggregation

    K Number
    K211108
    Date Cleared
    2021-06-04

    (51 days)

    Product Code
    Regulation Number
    892.1680
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Prudent 1717, Prudent 1417, Prudent 1212

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

    Indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding fluoroscopic, angiographic, and mammographic applications.

    Device Description

    The Prudent 1717, Prudent 1417, Prudent 1212 are digital radiography systems, featuring an integrated flat panel digital detector (FPD). It is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the updated panels are the same as our previous panel retaining the Wi-Fi wireless features and rechargeable battery operation. The Prudent 1717 is available in 3 pixel sizes: 100/140/168 um whereas the Prudent 1417, Prudent 1212 are available in two pixel sizes: 100/140 µm. The available resolutions vary according to the comparison table below. All of the models are Wi-Fi wired) and rechargeable battery (or AC line) operated. The device employs the same software as cleared in the predicate with only minor changes made.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the PIXXGEN Corporation's Prudent digital x-ray detector panels:

    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are implicitly derived from the comparison to predicate devices and adherence to established standards. The reported device performance is presented in comparison to these predicates.

    Acceptance Criterion (Implicit)Reported Device Performance (Prudent series)
    Image Quality (Quantitative)
    DQE (CSI) at 2 lp/mm (compared to K201932 alternate predicate: 45%)60%, 44%, 47% (for 100/140/168 um pixel sizes respectively). Same or better than alternate predicate.
    MTF (CSI) at 1 lp/mm (compared to K201932 alternate predicate: 35%)70%, 53%, 55% (for 100/140/168 um pixel sizes respectively). Better than alternate predicate.
    DQE (GOS) at 1 lp/mm (compared to K202995 alternate predicate: 20%)36%, 27%, 30% (for 100/140/168 um pixel sizes respectively). Better than alternate predicate.
    MTF (GOS) at 1 lp/mm (compared to K202995 alternate predicate: 50%)56%, 55%, 54% (for 100/140/168 um pixel sizes respectively). Better than alternate predicate.
    Limiting Resolution (compared to K182533 predicate: 3 lp/mm)5.0 lp/mm, 3.6 lp/mm, 3.0 lp/mm. Equal or better.
    Image Quality (Qualitative)
    Diagnostic Quality of Clinical Images (compared to predicate device)Excellent diagnostic quality. (As evaluated by a Board Certified Radiologist).
    Safety & Performance (Bench Testing & Other)
    Electrical Safety (IEC/UL 60601-1)Standards met.
    Electromagnetic Compatibility (IEC 60601-1-2)Standards met.
    Battery Safety (IEC 62133)Standards met.
    Risk Analysis (ISO 14971)Conducted in accordance with ISO 14971:2012.
    Software Validation (EN 62304)Software Validation Report for Revision 5 produced. The software remains essentially the same as in the predicate but moved from Revision 4 to Revision 5.
    Battery Life6-8 hours / 480-600 images. (Confirmed by testing, improved from predicate's 5 hours/300 images).
    Usability (IEC 62366-1)Evaluation concluded that the intended user can safely use the device in the intended environment without use error.
    Cybersecurity Labeling (FDA guidance)Cybersecurity precautionary labeling added.
    General Equivalence to Predicate (K182533) and Alternate Predicates (K202995, K201932)The results of clinical image inspection, bench, and test laboratory results indicates that the new device is as safe and effective as the predicate device. Clinical images collected demonstrate equal or better image quality as compared to our predicate. "Thus rendering them substantially equivalent to the predicate device."

    2. Sample Size Used for the Test Set and Data Provenance

    • Sample Size for Test Set: The document does not specify a numerical sample size for the "clinical images" test set. It only states "Clinical images collected."
    • Data Provenance: The document does not explicitly state the country of origin. It does not explicitly state if the data was retrospective or prospective. However, the term "Clinical images collected" typically implies prospective collection for such validation, but this is not explicitly confirmed.

    3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

    • Number of Experts: "a Board Certified Radiologist" (singular, implying one).
    • Qualifications of Experts: "Board Certified Radiologist." No specific experience level (e.g., "10 years of experience") is provided.

    4. Adjudication Method for the Test Set

    • Adjudication Method: The document states that the images were "evaluated by a Board Certified Radiologist." This suggests a single reader evaluation, which means no multi-reader adjudication method (like 2+1, 3+1) was explicitly performed or mentioned for the clinical image evaluation.

    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

    • No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned. The study described is an evaluation of the device's image quality by a single radiologist, not a comparison of human readers' performance with and without AI assistance. This device is a digital x-ray detector panel, not an AI-powered image analysis tool.

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

    • The document primarily describes a standalone performance evaluation of the imaging device itself (the detector panel) in terms of objective image quality metrics (DQE, MTF, limiting resolution) and a qualitative assessment of clinical images. Since the device is a detector, it intrinsically operates "standalone" in providing the image data. The "algorithm" here refers to the device's inherent image acquisition and processing capabilities, not an AI algorithm acting on those images. The evaluation by the radiologist is an assessment of the output of the standalone device.

    7. The Type of Ground Truth Used

    • For Quantitative Metrics (DQE, MTF, Limiting Resolution): These are objective physical measurements governed by established scientific and engineering standards (e.g., Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices). The "ground truth" for these is the measurement itself, verified against the alternate predicate devices' published specifications.
    • For Clinical Images: The ground truth was established by expert consensus/evaluation by a "Board Certified Radiologist." The assessment was subjective, stating the images were "of excellent diagnostic quality." It is not directly pathology or outcomes data.

    8. The Sample Size for the Training Set

    • This document describes a medical device (digital x-ray detector panel), not an AI algorithm that requires a separate "training set" in the machine learning sense. Therefore, there is no mention of a training set sample size. The device's "training" refers to its design and engineering to meet specific technical specifications.

    9. How the Ground Truth for the Training Set Was Established

    • As a digital x-ray detector panel, the concept of a "training set" and establishing ground truth for it (in the AI/machine learning context) does not apply. The device's performance is driven by its physical components and embedded firmware/software, which are developed and verified through engineering principles and adherence to standards rather than algorithm training on a dataset.
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