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510(k) Data Aggregation

    K Number
    K231892
    Date Cleared
    2023-09-12

    (76 days)

    Product Code
    Regulation Number
    892.1680
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    This product is intended to generate digital radiographic images of the skull, spinal column, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position and the system is intended for use in all routine radiography exams. This device is not intended for mammographic applications.

    Device Description

    The Definium Pace Select is a Digital Radiographic System designed as a modular system with components that includes fixed table with tube-stand, wallstand, cleared wireless digital detector, X-ray tube, collimator, high kV generator and acquisition workstation in control room. The system generates diagnostic radiographic images which can be sent through a DICOM network for applications including printing, viewing, and storage. The components may be grouped into different configurations to meet specific customer needs.

    AI/ML Overview

    The FDA 510(k) summary for the Definium Pace Select, a Digital Radiographic System, indicates that clinical studies were not required to support substantial equivalence for this device. Therefore, a study proving the device meets specific acceptance criteria in terms of clinical performance (like sensitivity, specificity, or reader improvement with AI assistance) or a detailed clinical acceptance criteria table for such measures is not provided in this document.

    The submission focuses primarily on demonstrating substantial equivalence to its predicate device (Discovery XR656 HD) based on non-clinical tests, technological characteristics, and safety/effectiveness data. The device's safety and effectiveness were confirmed through design verification and validation testing.

    Here's a breakdown of the relevant information from the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance:

    Since clinical studies were not deemed necessary for this 510(k) submission, there isn't a table presenting clinical acceptance criteria (e.g., sensitivity, specificity) and corresponding device performance metrics in the document. The acceptance criteria focused on compliance with voluntary standards and successful completion of verification and validation testing.

    Acceptance Criteria CategoryReported Device Performance / Compliance
    Voluntary Standards (Non-Clinical)Device complies with listed standards:
    • ES 60601-1:2005/(R)2012 & A1:2012, C1:2009/(R)2012 & A2:2010/(R)2012
    • IEC 60601-1-2:2014[Including AMD 1:2021]
    • IEC 60601-1-3: 2021
    • IEC 60601-1-6: 2020
    • IEC 60601-2-54: 2018
    • IEC 62366: 2015 + AMD1:2020
    • ISO 10993-1: 2018
    • ISO 10993-5: 2009/(R)2014
    • ISO 10993-10: 2010/(R)2014
    • ISO 10993-18 Second edition 2020-01
    • PS 3.1 - 3.20: 2022d (DICOM set) |
      | Quality Assurance Measures | Applied: Risk Analysis, Requirements Reviews, Design Reviews, Unit level testing (Module verification), Integration testing (System verification), Performance testing (Verification), Safety testing (Verification), Simulated use testing (Validation). |
      | Risk Mitigation | Risks for new floor mounted positioners and image chain were evaluated, mitigated with design controls and labeling. Mitigations were verified and validated with acceptable results. |
      | Design Verification & Validation | Performed to confirm safety and effectiveness; test plans and results were executed with acceptable results. |

    2. Sample Size for Test Set and Data Provenance:

    No distinct "test set" in the context of clinical performance evaluation (e.g., images for diagnostic accuracy assessment) is mentioned because clinical studies were not required. The "testing" referred to in the document pertains to design verification and validation, which would involve hardware and software testing, rather than a clinical image dataset.

    3. Number of Experts for Ground Truth and Qualifications:

    Not applicable, as no clinical study requiring expert ground truth establishment for diagnostic accuracy was conducted for this submission.

    4. Adjudication Method:

    Not applicable, as no clinical study requiring adjudication of expert interpretations was conducted.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

    No MRMC study was conducted. The device is a digital radiographic system, and the submission emphasizes its substantial equivalence based on technological characteristics and safety, not on AI assistance to human readers.

    6. Standalone (Algorithm Only) Performance:

    Not explicitly detailed in terms of a standalone diagnostic performance study, as the device itself is an imaging system, not a standalone diagnostic algorithm. The "image processing with same algorithm" as the predicate device suggests the algorithms are part of the overall system functionality, not a separate AI diagnostic tool.

    7. Type of Ground Truth Used:

    For the non-clinical tests and design verification/validation, the "ground truth" would be established through engineering specifications, regulatory compliance standards, and functional requirements. For example, for radiation output, the ground truth would be the expected range defined by standards and the device's design. There is no mention of clinical ground truth (e.g., pathology, outcomes data) being used for this 510(k) submission.

    8. Sample Size for Training Set:

    Not applicable, as this device submission is for a digital radiographic system, not an AI/ML algorithm that typically requires a training set of data for development. The reference to "same algorithm" for image processing as the predicate implies existing, validated algorithms rather than newly trained ones.

    9. How Ground Truth for Training Set Was Established:

    Not applicable, as this device is not presented as an AI/ML system requiring a training set with established ground truth.

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