(54 days)
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.
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified in this document. The submission focuses on demonstrating substantial equivalence to a predicate PACS system (GE Centricity PACS) for its intended use of image display, editing, review, storage, printing, and distribution. | "Xeron PACS has been demonstrated to perform as intended." (General statement, no quantitative 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
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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.
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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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).