Search Results

The PowerDR™ Digital X-ray Imaging System is indicated for use as an X-ray imaging modality to acquire, process, display, quality assure and store digital medical X-ray images.

The PowerDR™ Digital X-ray Imaging System is indicated for use in general radiographic and fluoroscopic examinations of any anatomy for adult, pediatric, and neonatal patients. It is not indicated for use in mammography.

The PowerDR™ Console Application is a digital medical X-ray imaging system consisting of an X-Ray detector, computer hardware and the PowerDR™ software. The User supplies the X-Ray generator. The PowerDR™ Console Application is intended to enable a procedure of medical image acquisition, processing, display, quality assurance, and storage. The software interfaces to an X-Ray detector from variety of vendors to acquire raw pixel data. Its image-processing algorithms transform raw pixel data into diagnostic quality images and image sequences to aid the medical professional in diagnosis. For temporary storage, image data can be stored on the local computer. For long term storage, image data can be stored on a portable media device or a remote PACS (Picture Archive and Communication System) server. The PowerDR™ Digital X-ray Imaging System is intended for use in general radiographic and fluoroscopic examinations of any anatomy for adult, pediatric, and neonatal patients. It is not intended for use in mammography. The system can be sold with or without a computer, and with or without a compatible, previously cleared, digital receptor panel.

The provided text is a 510(k) Premarket Notification for the PowerDR™ Digital X-ray Imaging System. This type of submission focuses on demonstrating substantial equivalence to a previously legally marketed device (predicate device), rather than proving the device meets specific performance acceptance criteria through the kind of studies typically seen for novel AI/ML devices.

Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria for AI/ML performance.

Specifically:

• No table of acceptance criteria and reported device performance is provided because this is a substantial equivalence submission, not a performance validation against defined metrics for an AI/ML component. The "performance" demonstrated is that the new device operates similarly to the predicate device in terms of image acquisition, processing, display, quality assurance, and storage.
• No sample size for a test set or data provenance is mentioned in the context of an AI/ML performance study. The "test set" here refers to the validation of the system's ability to acquire and process images, not to a diagnostic performance evaluation of an AI algorithm. The document states "image inspection, bench, and test laboratory results" were used, and "Each available digital receptor panel has undergone a rigorous verification and validation procedure."
• No number of experts or qualifications of experts used for ground truth establishment for a test set. This is not an AI/ML diagnostic study.
• No adjudication method is mentioned, as there is no diagnostic ground truth establishment process described for an AI/ML algorithm.
• No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done because there is no AI assistance component to evaluate.
• No standalone (algorithm only) performance study was done; the focus is on the integrated system's functionality.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.) is not applicable in the context of an AI/ML performance study. The "ground truth" for this device relates to the technical specifications and image quality relative to the predicate device.
• No sample size for the training set is applicable; this is not an AI/ML algorithm that undergoes a training phase as typically understood.
• How the ground truth for the training set was established is not applicable for the same reason.

The core argument for the PowerDR™ system is that it is substantially equivalent to the predicate device (Nexus DRF Digital X-ray Imaging System, K130318) in terms of its intended use, technology, and safety and effectiveness. The evidence provided to support this is:

• Bench testing: "The results of image inspection, bench, and test laboratory results indicates that the new device is as safe and effective as the predicate devices."
• Use of previously cleared components: All compatible digital panels supported by PowerDR™ "have previously received FDA 510(k) clearances" and "undergone a rigorous verification and validation procedure."
• Compliance with FDA guidance documents: Specifically, guidance for software in medical devices, cybersecurity, and pediatric imaging information.
• Comparison chart: A detailed "Substantial Equivalence Chart" (Section 5) outlining similarities in identification, intended use, description, where used, image processing, image storage, image data source, configuration, primary digital panel support (multiple for proposed vs. one for predicate, with all proposed panels being previously cleared), system software, image data format, image presentation, application software, tracking X-ray dose, fluoro image processing, MultiRad image support, dose and processing auto optimization, quality assurance, DICOM 3.0 conformance, IHE Integration profile, power source, and computer platform.

Conclusion stated in the document: "After analyzing bench testing and risk analysis and compliance to the DICOM standard, it is the conclusion of Radiology Information Systems, Inc. that the PowerDR™ Digital X-ray Imaging System is as safe and effective as the predicate device, have few technological differences, and has the same indications for use, thus rendering it substantially equivalent to the predicate device."

In summary, this 510(k) submission does not describe an AI/ML device or a study validating AI/ML performance using acceptance criteria. Instead, it demonstrates substantial equivalence to a predicate device through bench testing and comparison of technical specifications.

Ask a specific question about this device

Acculmaging is a software module capable of taking an X-ray image generated by a CR or DR and producing a digitally enhanced image for projection radiography applications. Acculmaging is not indicated for use in mammography.

Acculmaging is a Dynamic Link Library (DLL) module that takes a raw X-rav image generated by a CR or DR as its input and produces a fidelity-quality image for diagnostic purposes. It interfaces with radiological software to analyze digital image data and optimize the processing parameters applied to enhance detail and thus images' diagnostic quality. Acculmaging is not a standalone module and does not implement any user interfaces; it provides a dedicated image processing function to a top-level application running in the Microsoft Windows operating system. It is bound into a parent application that provides user interfaces and dynamically loads the DLL module, forming an integrated process; and, it can also be linked to a service module to provide the image processing service to other top-level applications.

Here's an analysis of the acceptance criteria and study as described in the provided 510(k) summary:

Acceptance Criteria and Reported Device Performance

Study Details:

1. Sample Size used for the test set and the data provenance:

   • Sample Size: Eight image sets were presented.
   • Data Provenance: Not explicitly stated, but the context implies these were existing X-ray images, likely retrospective. No country of origin is mentioned.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of Experts: One expert.
   • Qualifications of Experts: The document states "an expert." No specific qualifications (e.g., years of experience, subspecialty) are provided.

3. Adjudication method for the test set:

   • Adjudication Method: Not applicable. Only one expert reviewed the images, so no adjudication among multiple readers was performed.

4. If a multi-reader multicase (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:

   • MRMC Study: No, an MRMC study was not done. The study involved a single expert comparing image sets processed by the proposed device and the predicate. It did not assess human reader performance improvement with AI assistance.

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

   • Standalone Study: Yes, in a sense. The core of the study was a qualitative comparison of the output images from the Acculmaging software (proposed device) against images processed by the predicate device. The expert's role was to evaluate these processed images for diagnostic quality and detail equivalence, rather than using the software for a diagnostic task.

6. The type of ground truth used:

   • Ground Truth: Expert opinion/consensus (from a single expert). The expert's answers to the two questions (diagnostic quality and equivalence of detail) served as the basis for the conclusion.

7. The sample size for the training set:

   • Training Set Sample Size: Not provided. The submission focuses on the performance comparison for regulatory clearance, not on the development or training of the algorithm itself.

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

   • Training Set Ground Truth: Not provided. As no information about a training set is given, the method for establishing its ground truth is also not mentioned.

Ask a specific question about this device

The RSVS is intended for use as an image display and keyboard cutry node on a DICOM 3.0 network. It's principal anticipated functions are to merge bitmap images from image sources with keyboard input patient information to form DICOM 3.0 patient image files and provide quality assurance review of such files.

The RIS Scan View System (RSVS) is a DICOM 3.0 compliant secondary capture image application. It can receive, store, transmit, and display images. It provides storage and query/remeval service using C-STORE Service. The RSVS issues a DICOM 3.0 association request, RSVS starts sending the image data to the storage service provider. In addition, it can merge information from its keyboard with bitmap images from imaging devices to create DICOM 3.0 image files. The RSVS is a Microsoft Window based application.

The provided text is a "Summary of Safety and Effectiveness" for the Radiology Information Systems, Inc. RIS Scan View System (RSVS). It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain any information about acceptance criteria, the results of a study demonstrating performance, sample sizes, expert qualifications, or ground truth establishment.

The document states:

• Intended Use: The RSVS is intended for use as an image display and keyboard entry node on a DICOM 3.0 network. Its principal anticipated functions are to merge bitmap images from image sources with keyboard input patient information to form DICOM 3.0 patient image files and provide quality assurance review of such files.
• Substantial Equivalence: The RSVS is claimed to be substantially equivalent to the WinRad Teleradiology System (K936179). The comparison table highlights functional similarities like input (Bitmap image), output (DICOM 3.0), store/forward capabilities, and display functions. The only difference noted is that RSVS does not have compression, while WinRad does.

Based on the provided text, I cannot answer the questions regarding acceptance criteria and the study that proves the device meets the acceptance criteria. The document focuses on regulatory approval through substantial equivalence rather than performance validation studies that would include such details.

Ask a specific question about this device