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Merge PACS™ is a Picture Archiving and Communication System (PACS) for multi-modality (CT, MR, PT, US, MG, BTO, CR, DR/DX, NM, XA, RF, secondary capture (SC), and other DICOM-compliant modalities) image processing and display, diagnostic reading and reporting, communication, printing, and storage of medical imaging studies and other patient data. Intended clinical users include radiologists, orthopedic and other surgeons, referring physicians, and other qualified medical professionals.

Merge PACS, a software medical device, is a standards-based medical imaging diagnostic workstation that serves as an adjunct to assist the clinician to view, read, and report their findings. Merge PACS processes and displays medical images from DICOM-compliant modalities. The device is designed to enable efficient workflows by maintaining clinicians' worklists and retrieving and managing studies for reading, reporting, communication, and storage.

Merge PACS software runs on off-the-shelf computer hardware and can be configured to operate standalone or to integrate with vendor-neutral imaging archives (VNAs) such as iConnect Enterprise Archive (iCEA) via DICOM protocol, for image storage, and with radiological and hospital information systems (RIS and HIS) and medical record systems (EMR, EHR, etc.) via HL7.

Merge PACS can be accessed from within the hospital or enterprise, or from remote locations via web-based access. Images viewed on mobile devices should not be used for diagnostic purposes.

The focus of this premarket notification is on the addition of the Region Analysis Area and Region Analysis Volume tools, which allows for additional clinical analysis of images, including volumetric Standard Uptake Value (SUV) calculation. There has been a minor change in the indications for use statement from the previous Merge PACS device, with the removal of the optional Reach component which is no longer offered. Additional non-significant changes since the previous submission will be discussed.

This document (K192455) is a 510(k) summary for a Picture Archiving and Communication System (PACS) called "Merge PACS." It describes the device, its intended use, and compares it to predicate devices to demonstrate substantial equivalence.

Acceptance Criteria and Device Performance (Based on provided text):

The document does not explicitly present a table of quantitative acceptance criteria for the Merge PACS device's performance, as it focuses on demonstrating substantial equivalence to a predicate device rather than presenting novel performance metrics. The primary "performance" discussed is the addition of new features and the continued meeting of existing specifications.

However, based on the narrative and the comparison table, we can infer some "acceptance criteria" and "performance" in terms of functionality and safety equivalence to the predicate. The key "significant change" introduced is related to SUV (Standardized Uptake Value) Calculation for PET images.

Here's an interpretation based on the information provided, framed as acceptance criteria and performance:

1. Table of Implied Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance:

• Test Set Sample Size: The document does not specify a separate "test set" sample size in the context of clinical performance evaluation (e.g., for diagnostic accuracy). The non-clinical testing performed includes "Testing on unit level," "Integration testing," and "Performance testing." These tests would have used various forms of data, but the specific volume or type of imaging data is not detailed.
• Data Provenance: The document does not provide details on the country of origin of data or whether it was retrospective or prospective. Given that no new clinical studies were required, any data used for internal verification and validation would likely be existing, retrospective data.

3. Number of Experts and Qualifications for Ground Truth:

• The document states: "The subject of this premarket submission, Merge PACS, did not require clinical studies to support substantial equivalence." Therefore, there is no information provided on the number or qualifications of experts used to establish ground truth for a clinical test set in the context of diagnostic performance. The ground truth for the functional verification and validation would be against product specifications and established DICOM standards.

4. Adjudication Method for the Test Set:

• Since no clinical studies were performed requiring human interpretation and ground truth establishment for diagnostic performance, no adjudication method (e.g., 2+1, 3+1) is mentioned or implied.

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

• No MRMC comparative effectiveness study was done or mentioned. The submission explicitly states, "The subject of this premarket submission, Merge PACS, did not require clinical studies to support substantial equivalence." The device is a PACS system, and the focus is on its basic functionality, image display, and measurement tools, rather than a direct diagnostic aid that would typically undergo such a study.

6. Standalone (Algorithm Only) Performance:

• This device is a PACS system, which is a tool for clinicians to view and manipulate medical images. It is not an AI diagnostic algorithm or an "algorithm only" device in the sense of providing automated diagnoses or confidence scores. Its "performance" is in its ability to correctly acquire, store, display, and process images and data. Therefore, the concept of a "standalone" (algorithm-only) performance is not directly applicable in the way it would be for a CADx or AI detection algorithm.
• The document implies that the device's measurement capabilities (e.g., SUV calculation) are part of the overall system and are validated through non-clinical means against expected mathematical outputs or standard benchmarks.

7. Type of Ground Truth Used:

• For the significant change (3D SUV calculation), the "ground truth" would likely be derived from:
  • Physics-based or Phantom Data: Verified calculations on known phantom images with defined SUV values.
  • Reference Standard Implementations: Comparison of calculated SUVs against established, validated algorithms (e.g., from the reference predicate Xelis Fusion or other industry-standard software).
  • Mathematical/Computational Verification: Demonstrating that the algorithms correctly implement the SUV calculation formulas.
• For the PACS functionalities in general, the ground truth for "acceptance" is often:
  • DICOM Conformance: Data transmission, storage, and display adhere to DICOM standards.
  • Software Requirements Specifications: The software performs as intended according to detailed design documents.
  • User Interface/Experience Expectations: The tools function as designed for user interaction.

8. Sample Size for the Training Set:

• As this is primarily a PACS system with new measurement tools, and not an AI/ML algorithm that requires a "training set" in the traditional sense, no information on a training set sample size is provided. The development process involved "Design Reviews," "Testing on unit level," "Integration testing," and "Performance testing," which are typical for software validation but do not typically involve a separate "training set."

9. How Ground Truth for Training Set Was Established:

• Given that there is no "training set" for an AI/ML algorithm, the question of how its ground truth was established is not applicable to this submission. The "ground truth" for the development and verification of the PACS system's features would be based on:
  • Medical standards and protocols (e.g., DICOM).
  • Mathematical accuracy for measurements (e.g., SUV calculation).
  • Functional requirements and specifications.
  • Comparison to the performance of predicate devices.

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