(36 days)
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:
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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.
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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.
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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.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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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.
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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.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable.
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The sample size for the training set: Not applicable. There is no AI algorithm being "trained" mentioned in this submission.
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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.
§ 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).