Search Results

This software is used with general purpose computing hardware for the storage, review, analysis, annotation, distribution, printing, editing, and processing of digital images and data acquired from imaging devices such as CR, CT, DX, MR, MG, NM, PT, RF, US, XA, film digitizers, and document scanners, and other DICOM devices. With appropriate hardware, the software is intended for use as a primary diagnostic, analysis, and review tool for use by trained healthcare professionals.

It is the user's responsibility to ensure image quality, lighting, and image compression ratios are suitable for the clinical application.

Digitized film should not be used for primary diagnosis in mammography. Lossy compression should not be used for primary diagnosis in mammography. Primary diagnosis on digital mammograms should not be done on any monitors other than those specifically cleared by the FDA for digital mammography applications. Film printing for digital mammography should not be performed on any printers other than those specifically cleared by the FDA for digital mammography applications.

The PowerServer software suite is used on general purpose computing hardware and includes the following components: PowerServer, PowerReader, Gateway, and PowerCache. As long as minimum hardware and operating system requirements are met, the user or system integrator is free to choose his/her own hardware platform.

PowerServer is a scalable storage and distribution system for clinical images and data. Images can be stored in lossless or lossy formats. The system is DICOM compliant for image storage, archiving, retrieval, and transmission, and communicates with other DICOM devices. The system also communicates with PowerReader workstations and PowerCache servers. Acquired image data is preserved as captured and changes to display definitions are saved as presentation states so that images may always be reverted back to their initial state. PowerCache is a caching server that communicates with PowerServer and serves PowerReader workstations. A single PowerCache can serve multiple PowerReader workstations, reducing network traffic between PowerReader workstations and PowerServer.

PowerReader is a workstation that views, edits, manipulates, annotates, analyzes, stores, and distributes images and data stored on PowerServer and PowerCache. PowerReader can connect directly to PowerServer and can also connect via PowerCache. PowerReader provides the user with the ability to import, transmit, print, display, store, edit, and process medical images and data.

Gateway is a stand-alone DICOM compliant workstation that views, edits, manipulates, annotates, analyzes, stores, and distributes images and data. Gateway provides the user with the ability to import, transmit, print, display, store, edit, and process medical images and data.

The provided 510(k) summary for K052460 (RamSoft's PowerServer, PowerReader, Gateway, PowerCache) does not contain specific acceptance criteria or an explicit study proving performance metrics against such criteria.

The document primarily focuses on establishing substantial equivalence to predicate devices, describing the device's functionality, and outlining the developmental testing measures taken. It does not include quantitative performance data, clinical study results, or benchmarks for accuracy, sensitivity, specificity, resolution, or other clinical metrics typically found in documents for AI/Dx devices.

Therefore, I cannot populate the table or answer most of the requested questions based on the provided text.

Here's an explanation of why the information is not present in the provided text:

• Device Type: This submission is for a Picture Archiving and Communication System (PACS) and associated components. These types of devices are primarily for image management, display, and workflow, not for automated diagnostic interpretation (like an AI algorithm for disease detection). Therefore, the regulatory requirements often focus on technical performance (e.g., DICOM compliance, image integrity, display capabilities, security, interoperability) rather than diagnostic performance metrics (e.g., sensitivity, specificity, AUC).
• Regulatory Pathway: A 510(k) submission generally aims to demonstrate "substantial equivalence" to a legally marketed predicate device, meaning it performs as safely and effectively as the predicate. For PACS, this often involves validating that the displayed images meet certain quality standards for human interpretation, and that the system correctly stores and retrieves data. It does not typically require a standalone clinical efficacy study in the same way an AI diagnostic algorithm would.
• Date: The submission date is 2005. The regulatory landscape and expectations for AI/ML devices, including detailed performance studies, have evolved significantly since then. In 2005, the concept of "AI" in medical devices as we understand it today was nascent, and most submissions for image processing or viewing systems would not include the level of detail requested in your prompt regarding AI-specific validation.

Information that can be extracted or inferred:

1. A table of acceptance criteria and the reported device performance:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Not provided. The summary mentions various types of testing (Unit, System, Validation, Performance Testing) but does not detail the nature of these tests in terms of data sets, sample sizes, or 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/Not provided. As this is a PACS system, not an AI diagnostic algorithm, "ground truth" as it pertains to clinical accuracy or disease diagnosis is not typically established for the device itself in this context. The system's purpose is to display images for human experts (healthcare professionals) to establish ground truth or make diagnoses. The testing described focuses on software quality and functional performance.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not applicable/Not provided. No adjudication method is mentioned, as the testing described is not clinical diagnostic performance testing.

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:

• No, not done. Such a study is not mentioned. AI assistance or diagnostic capabilities are not claimed or detailed for this PACS system. Its primary function is image management and display.

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

• Not applicable/No. This device is explicitly described as a tool for use by trained healthcare professionals as a "primary diagnostic, analysis, and review tool." It is not a standalone diagnostic algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

• Not applicable/Not provided. For the reasons mentioned above, clinical "ground truth" for diagnostic accuracy is not relevant to the described testing for this PACS system.

8. The sample size for the training set:

• Not applicable/Not provided. This device is a software suite for PACS, not a machine learning model that requires a "training set" in the context of AI. The software is developed based on requirements and tested for functionality, not "trained" on data to learn patterns.

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

• Not applicable. As there is no AI training set, there is no ground truth established for it.

Ask a specific question about this device