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510(k) Data Aggregation

    K Number
    K172681
    Manufacturer
    Date Cleared
    2017-10-06

    (30 days)

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

    K160579

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

    Edge Air Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

    Device Description

    Edge Air is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN (IEEE 802.11a/g/n/ac) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize Xray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by a separate console SW program (K160579 / Xmaru View V1 and Xmaru PACS/ Rayence Co., Ltd.) for a diagnostic analysis.

    AI/ML Overview

    This document describes the equivalence of the OSKO, INC. Edge Air Digital Flat Panel X-ray Detector to a predicate device, the Rayence Co., Ltd. 1717WCC. The focus is on demonstrating that the Edge Air performs similarly to the predicate device, rather than establishing novel acceptance criteria for improved diagnostic accuracy.

    Here's a breakdown of the requested information based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    Since this is a substantial equivalence submission, the "acceptance criteria" are primarily based on demonstrating similar performance to the predicate device. The performance metrics are reported in terms of comparison.

    Acceptance Criteria (Comparison to Predicate)Reported Device Performance (Edge Air vs. 1717WCC)
    Intended UseSame
    Detector TypeSame (Amorphous Silicon, TFT)
    ScintillatorSame (CsI:T1)
    Imaging AreaSame (17 x 17 inches)
    Pixel MatrixSame (3072 X 3072)
    Pixel PitchSame (140 um)
    ResolutionSame (3.9 lp/mm)
    A/D ConversionSame (14 / 16 bit)
    Preview TimeSame (
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    K Number
    K172682
    Manufacturer
    Date Cleared
    2017-10-06

    (30 days)

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

    K160579

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

    Edge is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general purpose diagnostic procedures. Not to be used for mammography.

    Device Description

    Edge digital flat panel X-ray detector is based on flat-panel technology. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

    The RAW files can be further processed as DICOM compatible image files by separate console SW (K160579 / XmaruView V1 and XmaruPACS/ Rayence Co.,Ltd.) for a radiographic diagnosis and analysis.

    AI/ML Overview

    The provided text describes the 510(k) submission for the "Edge Digital Flat Panel X-ray Detector" and its substantial equivalence to a predicate device, the "1717SCC_140µm". The performance testing described primarily focuses on demonstrating this equivalence through non-clinical (bench) testing and a clinical consideration report.

    Here's a breakdown of the requested information based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly present a table of "acceptance criteria" for the device's performance in the typical sense of a target value that must be met for approval. Instead, the acceptance criteria for the study proving substantial equivalence appears to be based on the similarity of performance characteristics to the predicate device.

    The performance characteristics used for comparison are:

    CharacteristicAcceptance Criteria (Implicit: Similar/Not Inferior to Predicate)Reported Device Performance (Edge)Predicate Device Performance (1717SCC_140µm)Remarks
    Intended UseSameDigital imaging solution for general radiographic system for human anatomy; replace film or screen-based systems; not for mammography.Digital imaging solution for general radiographic system for human anatomy; replace film or screen-based systems; not for mammography.Same
    Detector TypeSameAmorphous Silicon, TFTAmorphous Silicon, TFTSame
    ScintillatorSameCsI:TlCsI:TlSame
    Imaging AreaSame17 x 17 inches (3072 x 3072 matrix)140 type : 3072 x 3072Same
    Pixel MatrixSame3072 x 30723072 x 3072Same
    Pixel PitchSame140 µm140 µmSame
    ResolutionSimilar3.9 lp/mm3.9 lp/mmSame
    A/D conversionSame14 / 16 bit14 / 16 bitSame
    Preview TimeSame or better≤2≤2Same
    MTF (@1 lp/mm)Similar / Not inferior59.1 (%)58.2 (%)Similar (Edge marginally better)
    DQESimilar / Not inferior76 (%)74 (%)Similar (Edge marginally better)
    Data OutputSameRAW (convertible to DICOM 3.0)RAW (convertible to DICOM 3.0)Same
    Imaging SoftwareSameXmaru View 1 / Xmaru PACS (Version 2.0.0)Xmaru View 1 / Xmaru PACS (Version 2.0.0)Same
    DimensionsSame460 x 460 x 15.5 mm460 x 460 x 15.5 mmSame
    WeightSame4 kg4 kgSame
    ApplicationSameGeneral Radiology system or Portable systemGeneral Radiology system or Portable systemSame
    Clinical Image QualitySuperior or equivalent to predicateSuperior to predicate in spatial and soft tissue contrast resolution.-Clinical consideration by expert

    2. Sample size used for the test set and the data provenance

    The document states: "To further demonstrate the substantial equivalency of two devices, clinical images are taken from both subject devices and reviewed by a licensed US radiologist to render an expert opinion."

    • Sample Size: Not explicitly stated as a number of images or patients. It mentions "sample radiographs of similar age group and anatomical structures". This suggests a limited sample size rather than a large clinical trial.
    • Data Provenance:
      • Country of Origin: US (implied by "licensed US radiologist").
      • Retrospective or Prospective: Not explicitly stated, but the description "clinical images are taken from both subject devices and reviewed" and "sample radiographs... taken" suggests these were specifically acquired for this comparison, implying a prospective data collection for the purpose of the study.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    • Number of Experts: "a licensed US radiologist" - One (1) expert.
    • Qualifications: "licensed US radiologist". No specific years of experience or subspecialty are provided.

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

    • Adjudication Method: None mentioned. The single radiologist's opinion appears to be the sole basis for the clinical image quality assessment ("reviewed by a licensed US radiologist to render an expert opinion").

    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

    • MRMC Study: No, an MRMC study was not conducted. This study's purpose was to demonstrate substantial equivalence between two X-ray detectors, not to evaluate AI assistance for human readers. The device itself is an X-ray detector, not an AI algorithm for improving reader performance.
    • Effect Size: Not applicable as no such study was performed or needed for this device.

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

    • Standalone Performance: The device is an X-ray detector. Its performance is standalone in terms of image acquisition (MTF, DQE, etc.). The "clinical consideration" involved human review of the images produced by the device, but this was to compare the image quality of the device to its predicate, not to assess an AI's diagnostic performance without a human. The non-clinical tests (MTF, DQE) are indeed "standalone" measures of the detector's physical performance.

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

    • The term "ground truth" isn't explicitly used in the context of diagnostic findings. For the non-clinical tests (MTF, DQE), the "ground truth" is established by adherence to IEC 62220-1 standards for objective physical performance measurements.
    • For the "clinical consideration" part, the "ground truth" for image quality comparison was the expert opinion of a single licensed US radiologist. This is a subjective assessment of image characteristics ("spatial and soft tissue contrast resolution are superior"). There's no mention of a diagnostic "ground truth" (e.g., presence/absence of disease confirmed by pathology or other means) being established for the clinical images used for review. The focus was on comparing the quality of the images produced by the devices, not on the diagnostic accuracy of those images for specific conditions.

    8. The sample size for the training set

    • This information is not applicable and not provided. The "Edge Digital Flat Panel X-ray Detector" is an imaging hardware device, not an AI algorithm that requires a training set. The clinical consideration involved comparing images produced by two hardware devices.

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

    • This information is not applicable and not provided as the device is not an AI algorithm requiring a training set.
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