Search Results

Centricity PACS Web DX) PACS System by GE Healthcare Dynamic Imaging Solutions is a device that receives medical images (including mammograms) and data from various imaging sources. Images and data can be stored, communicallyd, processed and displayed within the system or across computer networks at distributed locations.

Lossy compressed matther mages and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using and TDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed argepted by FDA.

Typical users of this system are trained professionals, including but not the physicians, radiologists, nurses, medical technicians, and assistants.

Centricity PACS Web Diagnostic (Web DX) PACS System is an Internet bases software picture archiving and communications system that provides users with capabilities relating to the acceptance, transfer, display, storage, and digital processing of medical images (including digital mammograms). Centricity PACS Web Diagnostic (Web DX) PACS System includes features to access and manage medical imaging studies from cat-scan (CT), magnetic radiography (MR), ultrasound (US), nuclear medicine (NM), computerized radiography (CR), digital radiography (DR), digital mammography (DM), digital x-ray (DX), x-ray angiography (XA), PET scan (PT), and other imaging modalities. Centricity PACS IW ™ PACS System is designed to be deployed over conventional TCP/IP networking infrastructure available in most healthcare organizations and utilizes commercially available computer platforms (Intel Pentiumbased) and operating systems (Microsoft Windows 2000, Windows 98). The system does not produce any original medical images. All images located on Centricity PACS Web Diagnostic (Web DX) PACS System have been received from DICOM compliant modalities and/or systems.

The provided text is a 510(k) summary for the GE Healthcare Centricity PACS Web Diagnostic (Web DX) PACS System. This document focuses on demonstrating substantial equivalence to predicate devices for a Picture Archiving Communications System (PACS) rather than establishing novel performance claims for a specific algorithm.

Therefore, the document does not contain the detailed information typically found in studies designed to prove a device meets specific performance-based acceptance criteria for an AI or imaging diagnostic algorithm. The information requested in your prompt (acceptance criteria, specific performance metrics, sample sizes for test/training sets, ground truth establishment, expert qualifications, MRMC studies, standalone performance) is not applicable or available in this type of submission.

Here's a breakdown of why the requested information is not present and what the document does provide:

1. A table of acceptance criteria and the reported device performance: Not applicable. This PACS system is cleared based on functional equivalence to predicate devices, not specific diagnostic performance metrics of an algorithm. Its "performance" is in its ability to accept, transfer, display, store, and process medical images, which was verified through system verification and validation testing, not a comparative clinical study with acceptance criteria.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. The "testing" referred to is "Thorough system verification and validation testing" to ensure safe and effective use and substantial equivalence, not a clinical test set for an algorithm's diagnostic performance. There is no information on data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience): Not applicable. No ground truth for diagnostic performance was established for an algorithm.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

5. 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: Not applicable. This is a PACS system, not an AI-powered diagnostic tool requiring MRMC studies to show improved human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a PACS system, not a standalone diagnostic algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable.

8. The sample size for the training set: Not applicable. There is no AI algorithm being "trained" mentioned in this submission.

9. How the ground truth for the training set was established: Not applicable.

What the document does indicate regarding "testing":

• Substantial Equivalence: The primary "proof" is the demonstration of substantial equivalence to predicate devices (#K072986 IntegradWeb™ PACS System by GE Healthcare Dynamic Imaging Solutions and #K043415 Centricity PACS System by GE Medical Systems). This means its functions are comparable to already legally marketed devices.
• Non-Clinical Testing: "Thorough system verification and validation testing was performed to ensure the safe and effective use of the Centricity PACS Web Diagnostic (Web DX) PACS System." This would typically include software testing, integration testing, cybersecurity, and ensuring compliance with standards for image display, storage, and transfer, rather than diagnostic performance metrics.
• Limitations/Caveats: The document explicitly states limitations, for instance:
  • "Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations."
  • "Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA." This highlights that the system's role is in data management and display, and appropriate hardware and image types are crucial for diagnostic interpretation by human readers.

In summary, the K053018 submission for the Centricity PACS Web Diagnostic (Web DX) PACS System did not involve a study to prove an algorithm meets specific performance-based acceptance criteria because it is a PACS system, not an AI diagnostic algorithm or a device making primary diagnostic interpretations. Its clearance was based on demonstrating functional equivalence to existing PACS systems and general safety and effectiveness through system-level verification and validation.

Ask a specific question about this device

Centricity PACS IW ™ by GE Healthcare Dynamic Imaging Solutions is a device that receives medical images (including mammograms) and data from various imaging sources. Images and data can be stored, communicated, processed and displayed within the system or across computer networks at distributed locations.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

Centricity PACS IW ™ PACS System is an Internet bases software picture archiving and communications system that provides users with capabilities relating to the acceptance, transfer, display, storage, and digital processing of medical images (including digital mammograms). Centricity PACS System includes features to access and manage medical imaging studies from cat-scan (CT), magnetic radiography (MR), ultrasound (US), nuclear medicine (NM), computerized radiography (CR), digital radiography (DR), digital mammography (DM), digital x-ray (DX), x-ray angiography (XA), PET scan (PT), and other imaging modalities. Centricity PACS IW ™ PACS System is designed to be deployed over conventional TCP/IP networking infrastructure available in most healthcare organizations and utilizes commercially available computer platforms (Intel Pentium-based) and operating systems (Microsoft Windows 2000, Windows NT, and Windows 98). The system does not produce any original medical images. All images located on Centricity PACS IW ™ PACS System have been received from DICOM compliant modalities and/or systems.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the Centricity PACS IW™ PACS System meets those criteria:

Device Name: Centricity PACS IW™ PACS System

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain explicit, quantitative acceptance criteria with specific performance metrics (e.g., sensitivity, specificity, or objective measurements for image display quality). Instead, the validation appears to be qualitative, focusing on "substantial equivalence" and user experience.

• Acceptance, transfer, display, storage, and digital processing of medical images (including digital mammograms).
• Access and manage studies from various modalities (CT, MR, US, NM, CR, DR, DM, DX, XA, PET).
• Deployment over TCP/IP networks.
• Use of commercial computer platforms (Intel Pentium-based) and operating systems (Microsoft Windows 2000, NT, 98). | "Testing performed has shown that the Centricity PACS IW™ PACS System incorporating enhanced PET-CT user preferences is substantially equivalent to the above referenced predicate devices." |
  | II. Safe and Effective Use:
• No contact with the patient.
• Does not control life-sustaining devices.
• Provides ample opportunity for competent human intervention in image interpretation.
• Proper handling of mammographic images (no lossy compression for primary interpretation, requires FDA-approved 5 Mpixel monitor). | "Thorough system verification and validation testing was performed to ensure the safe and effective use of the Centricity PACS IW™ PACS System with enhanced PET-CT user preferences."
  "The information provided... has shown that the Centricity PACS IN™ PACS System with enhanced PET-CT user preferences is substantially equivalent to the predicate device and is safe and effective for its intended use." |
  | III. Enhanced PET-CT User Preferences:
• Improvement or equivalence in user experience with PET-CT specific functionalities compared to the predicate. | "A reader evaluation, consisting of 11 board certified Radiologist was conducted where they evaluated the new enhanced PET-CT user preferences to the predicate device on actual clinical cases." (Implicitly, the evaluation found them equivalent or improved enough for substantial equivalence). |

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

• Sample Size: The text states "actual clinical cases" were used, but the specific number of cases or images in the test set is not provided.
• Data Provenance: The origin of the data (e.g., country) is not specified. It refers to "actual clinical cases," implying retrospective data.

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

• Number of Experts: 11 board-certified Radiologists.
• Qualifications: "board certified Radiologist" (no specific number of years of experience mentioned).

4. Adjudication Method for the Test Set

The text describes a "reader evaluation" where 11 radiologists "evaluated the new enhanced PET-CT user preferences to the predicate device." It does not explicitly state an adjudication method (like 2+1 or 3+1 consensus). It implies that their collective evaluation or individual feedback contributed to the substantial equivalence conclusion rather than a formal ground truth adjudication process for specific diagnostic decisions.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size.

• Was it done? Yes, a form of reader comparison was performed. The "reader evaluation, consisting of 11 board certified Radiologist was conducted where they evaluated the new enhanced PET-CT user preferences to the predicate device on actual clinical cases." This compares the experience with the new system's features against a predicate.
• Effect Size of human readers' improvement with AI vs. without AI assistance: This study does not measure improvement with AI assistance. The device is a PACS system, a tool for displaying and managing images, not an AI diagnostic algorithm. The study compared user preferences and the experience of using the PACS features (specifically "enhanced PET-CT user preferences") between the new device and a predicate device. Therefore, there's no "AI vs. without AI assistance" effect size to report here.

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

• Was it done? No, a standalone algorithm performance study was not conducted. The Centricity PACS IW™ PACS System is a Picture Archiving Communications System (PACS), which is a display and management system for medical images, not a diagnostic algorithm that provides standalone interpretations. The system "does not produce any original medical images" and "does not contact the patient, nor does it control any life sustaining devices." Human interpretation is explicitly part of the intended use.

7. The type of ground truth used.

The "ground truth" for this study was based on the evaluation and feedback of 11 board-certified radiologists regarding the "enhanced PET-CT user preferences" on "actual clinical cases." This is more akin to a usability or comparative user experience evaluation rather than a ground truth for diagnostic accuracy (e.g., pathology, clinical outcomes, or expert consensus on disease presence). The study aims to show that the new system's features are at least equivalent in utility and user experience to those of the predicate device.

8. The sample size for the training set.

The document refers to "Thorough system verification and validation testing" and a "reader evaluation." It does not mention a training set in the context of an algorithm. This device is a PACS system, not a machine learning model, so the concept of a "training set" for an algorithm doesn't apply.

9. How the ground truth for the training set was established.

As there is no mention of a training set for an algorithm, the method for establishing its ground truth is not applicable and not provided.

Ask a specific question about this device