Search Results

The FirstView™ is intended to display and manipulate x-ray images.
The FirstView™ is indicated for displaying and manipulating chest x-ray images. It acts as an interface with the CAD component, if installed or available at the clinical site, and provides display of information contained within the DICOM header of the images. This device is intended to be used by trained professionals, such as physicians, radiologists, and technicians.

The FirstView™ consists of a dedicated server that has been programmed with a database and server software, as well as client software that is loaded onto an existing workstation. New images, which may be a chest x-ray or CAD result, are sent to the FirstView™ server from a Digital or from the PACS network, prompting FirstView to query the PACS for additional images related to that study. The FirstView™ server manages the images within its Riverain proprietary database.

The provided text concerns the 510(k) summary for the FirstView™ device, a system for displaying and manipulating chest x-ray images. However, the document does not contain information related to specific acceptance criteria, formal study design, sample sizes for test or training sets, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies that would typically be described as "proving the device meets the acceptance criteria."

Instead, the document focuses on the device's substantial equivalence to predicate devices for its intended use as a "Picture archiving and communications system" (PACS). The 510(k) process primarily evaluates whether a new device is as safe and effective as a legally marketed predicate device, rather than requiring extensive clinical trials to meet novel performance criteria in the way a PMA (Premarket Approval) might.

Therefore, many of the requested sections regarding acceptance criteria and study details cannot be filled from the provided text. The information available is presented below within the constraints of the provided document.

Acceptance Criteria and Device Performance

The document does not explicitly state quantitative acceptance criteria or report specific device performance metrics in the format typically used for demonstrating performance against such criteria (e.g., sensitivity, specificity, accuracy).

Instead, the "acceptance criteria" in this context appear to be met by demonstrating substantial equivalence to legally marketed predicate devices.

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • The document does not report any specific test set sample size or data provenance (e.g., country of origin, retrospective/prospective). The 510(k) submission primarily relies on comparison to predicate devices, not independent performance testing against a specific dataset.

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

   • The document does not mention a test set with ground truth established by experts.

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

   • The document does not describe any adjudication method for a test set.

4. 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:

   • The document does not describe an MRMC comparative effectiveness study. The FirstView™ is described as an image display and manipulation system, potentially interfacing with CAD, but not inherently as an AI-powered diagnostic aid that would typically undergo such a study to evaluate human reader improvement.

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

   • The document does not describe any standalone algorithm performance study.

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

   • The concept of "ground truth" for a specific performance evaluation test set is not mentioned in the document.

7. The sample size for the training set:

   • The document does not report any training set sample size.

8. How the ground truth for the training set was established:

   • The document does not report how ground truth for a training set was established, as it does not mention a training set.

Ask a specific question about this device

The Xeron PACS is a medical imaging software application intended to display, edit, review, store, print and distribute images acquired from imaging devices such as Computed Tomography (CT), Magnetic Resonance (MR), Computed Radiography (CR), Ultrasound (US), Nuclear Medicine (NM), and other devices.

Xeron PACS is an image management system that allows authorized personnel to acquire, display, edit, review, store, print, and distribute standard Digital Imaging and Communications in Medicine (DICOM) medical images within a Picture Archiving and Communication System (PACS) environment. The Xeron PACS system consists of five software components:

• · XP WebStation
• · XP RadStation
• · XP Site Administrator
• · XP Data Router
• · XP Server Manager
  Xeron PACS supports DICOM network structures which allow for efficient medical image acquisition from any medical modality, including CT, MR, CR, and others. Distribution of images is provided to desktop computer systems, computer systems connected by intranet or internet, hardcopy devices, and Hospital Information Systems (HIS).

Here's an analysis of the provided text regarding acceptance criteria and the supporting study:

The provided document, K062757, is a 510(k) Premarket Notification for the "Xeron PACS" system. A 510(k) submission primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving performance against specific acceptance criteria through a detailed clinical study with performance metrics.

Based on the information given, the document does not contain explicit acceptance criteria or a detailed study report that proves the device meets specific acceptance criteria in the manner typically seen for performance claims (e.g., sensitivity, specificity, or reader improvement).

Instead, the submission focuses on:

• Device Description: What the device is and what it does.
• Intended Use: The purpose and scope of the device.
• Technological Comparison: How it is similar to the predicate device.
• Testing: A very general statement that says "Xeron PACS has been demonstrated to perform as intended."
• Conclusion: That it is substantially equivalent to legally marketed Image Processing Systems (PACS).

Therefore, many of the requested details about acceptance criteria and study parameters cannot be extracted from this specific 510(k) summary.

Here's a breakdown of what can be inferred or explicitly stated based on the provided text, and where information is not present:

1. Table of Acceptance Criteria and Reported Device Performance

No explicit acceptance criteria or quantitative performance metrics are provided in the document. The "performance" demonstrated is a general claim of "performing as intended" and being "substantially equivalent" to a predicate PACS system. PACS systems are infrastructure devices, and their "performance" often relates to data handling, display capabilities, and adherence to standards (like DICOM), rather than diagnostic accuracy metrics.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

Given the nature of a PACS system, the "test set" would likely refer to a set of DICOM images used to verify its functionality (acquisition, display, storage, distribution). However, the document does not elaborate on this.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

Establishing "ground truth" with expert consensus is typically relevant for interpretative AI/CAD devices. For a general PACS system, ground truth would relate to the integrity and accurate display of the medical images themselves, which usually doesn't require expert consensus in the same way.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

This is not applicable as no expert-adjudicated test set is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC study was not done (or at least not reported in this summary). The document does not describe any studies involving human readers or comparative effectiveness with or without AI assistance.

• Effect Size of Human Reader Improvement: Not applicable, as no such study was conducted.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Standalone Study: Not explicitly described in terms of diagnostic performance metrics for an algorithm. The "testing" mentioned is likely functional and performance testing of the software system as a whole, addressing its ability to acquire, store, display, and distribute images, rather than a standalone diagnostic algorithm performance study.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. For a PACS system, "ground truth" would generally refer to the correct and compliant handling of DICOM images and associated metadata, and the accurate representation of image data. This is typically verified through technical validation against DICOM standards and internal functional specifications. Medical "ground truth" from pathology or outcomes data is not relevant for a general PACS device.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. The Xeron PACS is described as an "image management system" and "software application," not an AI/ML algorithm that requires a training set in the conventional sense.

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

• How Ground Truth Was Established for Training Set: Not applicable, as there is no apparent training set for an AI/ML algorithm.

Summary of Missing Information:

The provided 510(k) summary for Xeron PACS is a regulatory document focused on demonstrating substantial equivalence to a predicate device. It does not provide the detailed performance study information common for AI/CAD devices or devices with specific diagnostic claims. The "testing" mentioned is a general statement, and no specific acceptance criteria, sample sizes for medical image sets, expert qualifications, or ground truth methodologies for diagnostic purposes are presented.

Ask a specific question about this device