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RADSpa™ components are intended to be used in hospitals, imaging centers, radiologist reading practices and by any user who requires and is granted access to view patient's images, demographic and report information.

RADSpa™ PACS is a flexible, standards-compliant, web-based, workflow management solution designed for centralized and distributed imaging environments. This device consolidates all radiology exam information including images and reports from multiple systems into a centrally managed work list, which can be accessible using any browser. RADSpa™ PACS provides complete PACS functionality used to receive and manage DICOM images and make the data available across a network. RADSpa™ viewer is used for diagnosis and Primary Image Interpretation of DICOM compliant image data derived from all modalities. This component is not intended for mobile devices.

RADSpa™ MPR/MIP/3D components are used for post-processing and is designed to assist radiologists in the diagnostic analysis, visualization, and quantification of CT and MR images. This device supports enhanced visualization and analysis techniques such as multi-planar and oblique reformats, maximum intensity projections, image averaging, subtraction and blinking of images acquired at different time points.

RADSpa™ MPR/MIP/3D is also used to perform post-processing analysis digital images from CT and MR. The software analysis tools may be applied to image subtractions, reformatted images, multi-planar reformats and maximum intensity projections. The software package includes tools to allow the radiologist to manipulate and fly-through images for enhanced visualization.

RADSpa™ MPR/MIP/3D is not intended for mobile devices.

RADSpa™ is a software device that consists of RADSpa™ PACS and Viewer&RADSpa™ 2D/MPR/MIP/3D viewer.

RADSpa™ PACS components (server-side) enable receiving, storing and sending DICOM images and managing the workflow required for radiologists. The components are developed using .NET and runs on Windows OS. The images are stored in the file system and the workflow-related data in a RDBMS database

RADSpa MPR/MIP/3Dviewer components run on Windows OS and enable downloading of images from RADSpa™ server and viewing and manipulating of those images on the workstation

Overall features include:

• Centralized or Distributed Archive functionality
• Uses commercially available computers, servers, operating systems and network infrastructure, with expandable storage capability
• Single or Multi server options (i.e. Archive, Web Server and PACS application can reside on a single server computer)
• Pre-emptive downloading- perfecting of images in real time Web based solution
• Scalable from single practice to enterprise wide PACS
• High level of security
• DICOM, JPEG and JPEG 2000 compliant

The provided document is a 510(k) summary for the RADSpa™ system, a Picture Archiving Communication System (PACS) with viewing and post-processing capabilities. While it details the device description, intended use, and substantial equivalence to predicate devices, it does not contain information about specific acceptance criteria, performance study results, or validation methodology (e.g., sample size, expert qualifications, ground truth establishment, or multi-reader multi-case studies) for the RADSpa™ device itself.

The document primarily focuses on establishing substantial equivalence to existing PACS systems (ResolutionMDTM 2.1 and eRAD PACS) by comparing features and intended use. The conclusion states that the submission contains "adequate information and data to enable FDA - CDRH to determine substantial equivalence to the predicate device" and mentions a "hazard analysis" and "Level of Concern for potential hazards has been classified as 'minor'". This indicates that the regulatory approval was based on demonstrating equivalence rather than the submission of a new clinical performance study with specific acceptance criteria and results.

Therefore, I cannot extract the requested information from the provided text. The document does not describe:

1. A table of acceptance criteria and reported device performance.
2. Sample size, data provenance, number of experts, qualifications, or adjudication methods for a test set.
3. Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or its effect size.
4. Standalone (algorithm only) performance.
5. The type of ground truth used.
6. The sample size for the training set.
7. How the ground truth for the training set was established.

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This single use sterile equipment cover is intended for use by professionals in a sterile clinical setting to cover and prevent contamination of non-patient contact equipment during various procedures.

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The provided text is a 510(k) premarket notification letter from the FDA regarding the "RadScan Equipment Slicker®". This type of document determines substantial equivalence to a predicate device and approves the device for market. It does not contain information about acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert reviews as requested in your prompt. These details would typically be found in the manufacturer's submission to the FDA, which is not included here.

Therefore, I cannot provide the requested information based on the given document.

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The RadStar DDR Digital Imaging System is intended for use in generating radiographic images of human anatomy. This device is intended to replace film/screen systems in all general purpose diagnostic procedures. This device is intended for incorporation into a complete x-ray system by qualified x-ray service personnel. This device is not intended for mammography applications. This device is intended for use by qualified medical personnel and is contraindicated when, in the judgment of the physician, procedures would be contrary to the best interest of the patient.

The RadStar Digital Imaging System consists of two components, a solid state x-ray imager and software for viewing the captured images on a Windows-based computer. The device is intended for incorporation into a complete x-ray system by qualified x-ray service personnel. The RadStar Digital Imaging System will display high quality images in less than 5 seconds over a wide range of X-Ray dose settings.

This document is a 510(k) summary for the RadStar Digital Imaging System, describing its substantial equivalence to previously marketed devices. It does not contain the typical elements of a study proving a device meets specific acceptance criteria as it would for an algorithm or AI-powered device.

For medical devices that generate images, "acceptance criteria" are usually related to image quality metrics, safety, and performance against predicate devices. The study involved in such a submission primarily aims to demonstrate substantial equivalence to a predicate device, rather than proving performance against pre-defined quantitative acceptance criteria for a new and innovative function.

Based on the provided text, here's an analysis:

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

The provided text does not define specific quantitative "acceptance criteria" for performance metrics like sensitivity, specificity, accuracy, or image quality scores. This submission is for a conventional digital X-ray detector, and its performance demonstration focuses on substantial equivalence rather than novel algorithm performance.

The submission states:

• "The RadStar Digital Imaging System will display high quality images in less than 5 seconds over a wide range of X-Ray dose settings."
• "The technological characteristics between the predicate and proposed devices are identical. There is no difference in fundamental scientific technology."
• "There are no significant differences between the RadStar Digital Imaging System and the predicate devices and therefore, the RadStar Digital Imaging System does not raise any questions regarding safety and effectiveness."
• "The RadStar Digital Imaging System, as designed, is as safe and effective as the predicate device, and the device is determined to be substantially equivalent to the referenced predicate device currently on the market."

These statements serve as the "performance" claim, but they are qualitative and comparative to existing technology, not quantitative against specific, pre-defined acceptance criteria for diagnostic efficacy.

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. This is a submission for a digital X-ray imager, not an AI or algorithm-based diagnostic tool requiring a test set of patient data with ground truth. The "test" for this device would involve engineering and physical performance evaluations (e.g., image resolution, DQE, MTF, dose response) rather than a clinical study with patient images to evaluate diagnostic accuracy.

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, as there is no mention of a "test set" of medical images requiring expert ground truth establishment.

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

Not applicable, as there is no mention of a "test set" requiring adjudication.

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 not an AI-assisted device. The submission focuses on the digital imaging system itself replacing film/screen systems.

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

Not applicable. This is not an algorithm, but a hardware imaging system with associated viewing software. Its performance is inherent to its image acquisition capabilities.

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

Not applicable. There is no "ground truth" in the clinical sense for this type of device submission. Performance is assessed through technical specifications and comparison to predicate devices.

8. The sample size for the training set

Not applicable. This device does not use machine learning or AI that requires a "training set."

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

Not applicable, as there is no training set for this device.

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