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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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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, technologists, and other qualified medical professionals. Data can be received directly from acquisition modalities, CAD systems, and other image processing systems, or indirectly via importing. Data that is not DICOM-compliant, such as photos, can be converted to DICOM format by Merge PACS.

Merge PACS provides image manipulation tools to enable users to view and compare images such as: linkinq, MPR, MIP, 3D image fusion/registration of CT, MR, and PET; as well as CVR (Color Volume Rendering), measurements (linear distances, angles, areas, SUV, etc.), and annotations (for example, outline and label regions of interest, label spinal vertebrae).

The Real Time Worklist (RTWL) displays the real-time status of radiology activity and provides customizable workflow management capabilities. Communication of critical results is facilitated and documented through optional, configurable components.

The Patient Dashboard provides a composite view of patient data, both imaging and non-imaging. The optional Reach component provides clinicians with secure, proactive communication and access to clinical reports and images. Multi-tier patient identity matching provides a comprehensive view even when dealing with multiple disparate patient identities.

Order and report information generated by the HIS/RIS and report creation systems are received and displayed via the transmission of HL7 messaging. Lossless (reversible) and lossy (irreversible) image compression are supported for viewing, storage and communication.

Merge PACS displays full fidelity DICOM images for use in the diagnostic interpretation of mammography using MG or BTO images. Thick slab MIP presentation can be applied to BTO images.

Lossy compressed images and digitized screen film images must not be used for primary diagnosis of mammography studies, and only display monitors that have regulatory clearance for mammography interpretation should be used for the interpretation of mammography studies.

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) for image storage, and with radiological and hospital information systems (RIS and HIS) and medical record systems (EMR, EHR, etc.).

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

The focus of this premarket notification is on the addition of the ability to "fuse" images for viewing (image fusion) and on the ability to measure Standardized Uptake Values (SUV) on PET (Positron Emission Tomography) images.

The provided text is a 510(k) Pre-market Notification for the Merge PACS™ system. It outlines the device's intended use, comparison to predicate devices, and performance data. However, it explicitly states that clinical studies were not required, meaning specific acceptance criteria with reported performance, sample sizes, and expert reviews as requested in your prompt were not applicable or performed for this submission.

The document focuses on demonstrating substantial equivalence to existing devices (AMICAS PACS 6.0 and Fujifilm Synapse PACS) by comparing features and technologies, rather than proving performance against predefined quantitative acceptance criteria through clinical trials.

Therefore, many of the requested details cannot be extracted from this document. Here's what can be provided based on the text:

Key Takeaway: The submission is based on demonstrating substantial equivalence through feature comparison and non-clinical testing, not on meeting specific quantitative acceptance criteria established via clinical studies.

Summary of Device Performance (Based on Non-Clinical Testing and Feature Comparison):

Since no clinical studies with specific acceptance criteria were conducted, the "performance" is described in terms of compliance and functional validation.

• Acceptance Criteria and Reported Device Performance: Not applicable as defined by your prompt (no quantitative clinical acceptance criteria provided or met). The "acceptance" is based on functional verification through non-clinical testing and comparison to predicates.

  Table (Hypothetical, based on functional claims, not quantitative performance):

• Sample size for the test set and data provenance: Not specified for any quantitative testing that would establish "test set" performance. The non-clinical testing would have involved internal datasets, but details on size, origin, or retrospective/prospective nature are not provided.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No ground truth established by experts for performance evaluation was mentioned. The "ground truth" for the non-clinical tests would have been the expected functional output.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No expert adjudication method was mentioned.
• 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: No MRMC study was done. The device is a PACS system, not an AI for diagnostic assistance in the traditional sense that "improves human readers."
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device is a software workstation with various functionalities, not a standalone AI algorithm with a distinct "performance" metric that would be evaluated in isolation from human interaction. Its functions (like SUV calculation) are tools for human users. Non-clinical tests confirmed these functions performed "as expected," which is a form of standalone functional validation.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the non-clinical tests, the "ground truth" was likely the expected outcome of direct functional tests (e.g., if you input X, the SUV calculation should yield Y; if you overlay two images, they should align based on DICOM coordinates). There was no mention of using clinical outcomes, pathology, or expert consensus as a ground truth for performance evaluation in the context of "acceptance criteria."
• The sample size for the training set: Not applicable. The document describes a PACS system which is a software medical device, not a machine learning or AI model that typically has a "training set." The development of the software would involve traditional software engineering and testing processes, not a dataset-driven training phase in the AI sense.
• How the ground truth for the training set was established: Not applicable, as there is no "training set" in the context of an AI/ML model for this device.

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