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510(k) Data Aggregation

    K Number
    K100837
    Date Cleared
    2010-12-03

    (254 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    PaCentric is a software only device intended for viewing of images acquired from CT, MR, CR, US and other DICOM compliant medical imaging systems when installed on suitable commercial hardware. Images and data can be captured, stored, communicated, processed, and displayed within the system and or across computer networks at distributed locations. The device is not intended for clinical viewing of mammography images.

    Device Description

    PaCentric Web is an Internet application for read-only review of external image deliveries. PaCentric Web facilitates the display and/or transfer of clinical DICOM images and reports to a Web browser. PaCentric is ensuring secure and time-limited access to clinical data. The receiver will only have access to the relevant data for a particular delivery. Using PaCentric, patient data can be delivered to a specific recipient anywhere in the world. PaCentric Web gives a qualified user read-only access to an image delivery. Access is given with a key and password, which must be provided by the Sender. PaCentric Web features commonly used tools and features found on DICOM workstations: Preview image icons from which the user may select images to review in full size; Measurements: Distance, Area, Volume, Angle and ratios. Measurement calibration based on information provided by the relevant DICOM tag. The measurements are performed by having the user select a caliper and marking end points by clicking with a mouse button. The results are being displayed and updated in a results area on the screen; Horizontal and Vertical invert, i.e. flipping the image sideways or upside-down; Color maps. A pixel consists of three sets of values from 0 to 255 that together constitute a color or a gray level. Technically, these RGB values are reduced according to desired color hue. A grayscale image therefore will not lose any details in the image for higher or lower values. This function is mostly used for MONOCHROME1 and MONOCHROME2 images; Window-leveling and Brightness control. These Attributes is only used for Images with Photometric Interpretation (0028,0004) values of MONOCHROME1 and MONOCHROME2. They have no meaning for other Images. When working with DICOM images, the user has the ability to make changes to the displayed images, on a global level, by manipulating the Window Level. Tonal changes to specific areas of an image. Window Center DICOM TAG 0028,1050 and Window Width DICOM TAG 0028,1051 specify a linear conversion from stored pixel values (after any Modality LUT or Rescale Slope and Intercept specified in the IOD have been applied) to values to be displayed. Window Center contains the input value that is the center of the window. Window Width contains the width of the window. Note: The terms "window center" and "window width" are not consistently used in practice, nor were they defined in previous versions of the standard. The definitions here are presented for the purpose of defining consistent meanings for identity and threshold transformations while preserving the common practice of using integral values for center and width. Zoom - Image is downloaded in its original size, but the height and width definition can be adjusted and will therefore function as zoom. Language selection. Default language is based on current location derived from the IP address of the viewer. Simple printable report with preview and selectable fields: Display of patient demographics: Sex, age, date of examination, nationality, operator, weight, referring physician, height, performing physician, body surface area (BSA), location, BP, Sender address, Description, Diagnosis, Comments, Images, selectable signature lines. Ability to display up to eight independent image areas at the same time.

    AI/ML Overview

    The provided document is a 510(k) Pre-market Notification Summary for PaCentric Web, a software-only device intended for viewing DICOM-compliant medical images. This summary indicates that the device is a picture archiving and communications system (PACS) and workstation.

    Here's an analysis of the acceptance criteria and study information, based solely on the provided text.

    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly state quantitative acceptance criteria or device performance metrics for PaCentric Web in the traditional sense of a clinical claims study. Instead, it focuses on the device's adherence to voluntary standards and quality assurance measures applied during development.

    The "Test Summary" section lists the following quality assurance measures:

    • Risk Analysis
    • Requirements Reviews
    • Design Reviews
    • Testing on unit level (Module verification)
    • Integration testing (System verification)
    • Final acceptance testing (Validation)
    • Performance testing

    While these are testing activities, they are not presented with specific numerical acceptance thresholds or measured performance values (e.g., sensitivity, specificity, accuracy, processing speed, image quality metrics) that would typically be a part of a clinical performance study. The description does not provide quantifiable results that could be placed in a "Reported Device Performance" column.

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

    The document does not provide specific details on the sample size used for any test sets, nor does it mention the data provenance (e.g., country of origin of the data, retrospective or prospective) for any testing performed. The "Test Summary" lists various testing phases but without the associated data characteristics.

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

    The document does not mention the use of experts to establish ground truth for any test set. The type of device (read-only PACS viewer) suggests that the primary "ground truth" would be the integrity and accurate display of the DICOM images themselves, rather than diagnostic outcomes requiring expert consensus.

    4. Adjudication Method for the Test Set

    Since the document does not describe a test set requiring ground truth establishment by experts, there is no mention of an adjudication method (such as 2+1, 3+1, or none).

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

    The document does not mention that a multi-reader multi-case (MRMC) comparative effectiveness study was done, nor does it provide an effect size of how much human readers improve with AI vs without AI assistance. This type of study is not typically applicable for a read-only PACS viewer.

    6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

    The document does not describe a standalone performance study in the context of an algorithm's diagnostic accuracy or performance without human intervention. The device itself is a tool for human viewers, so "standalone performance" in the AI sense is not applicable or discussed.

    7. Type of Ground Truth Used

    Given that PaCentric Web is a read-only PACS viewer, the "ground truth" would relate to the correct display and measurement capabilities of the software as per DICOM standards, rather than diagnostic "ground truth" like pathology or outcomes data. The document implies that testing focused on system functionality, image display accuracy as per DICOM tags, and the correct execution of features like measurements, window-leveling, and zoom. However, the specific type of ground truth used to validate these functions (e.g., reference measurements on calibrated images, comparison to established DICOM viewers) is not explicitly stated.

    8. Sample Size for the Training Set

    The document describes PaCentric Web as an "Internet application" and a "software device" for viewing images. It is not an AI/ML device that typically involves a "training set" for model development. Therefore, no information on the sample size for a training set is provided.

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

    As the device is not an AI/ML product developed with a training set, the question of how ground truth was established for a training set is not applicable and therefore not addressed in the document.


    Summary of Acceptance Criteria and Study Details (Based only on the provided text):

    FeatureDetail Provided in Document
    Acceptance Criteria & Performance TableAcceptance Criteria: The document states "PaCentric Web complies with the voluntary standards as detailed in Section 9." (Section 9 is not provided in the extract). It also lists quality assurance measures: Risk Analysis, Requirements Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Final acceptance testing (Validation), Performance testing.

    Reported Device Performance: No specific quantitative performance metrics (e.g., accuracy, speed) or pass/fail thresholds are reported in the provided text. The document indicates that these quality measures were "applied to the development." |
    | Test Set Sample Size & Data Provenance | Not specified. |
    | Experts for Ground Truth & Qualifications | Not applicable/Not mentioned. The device is a viewer; expert consensus for diagnostic ground truth is not relevant in the context described. |
    | Adjudication Method | Not applicable/Not mentioned. |
    | MRMC Comparative Effectiveness Study | No, not mentioned. |
    | Standalone Performance Study | No, not mentioned. |
    | Type of Ground Truth Used | Implied to be the integrity and accurate interpretation/display of DICOM image data and associated metadata according to DICOM standards. Specific methods or reference standards for validating these functionalities are not detailed. |
    | Training Set Sample Size | Not applicable (not an AI/ML device). |
    | How Training Set Ground Truth Established | Not applicable (not an AI/ML device). |

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