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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.

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The intended use of this device (power/manual wheelchair) is the same as the predicate device, the Commuter (K934232). The intended use for the power/manual wheelchair is to provide mobility to physically impaired individuals. These chairs will allow the user to have a battery powered wheelchair or they can disengage the drive mechanism and have a manual wheelchair.

The specific medical conditions for which the device is indicated are listed as, but not limited to:

Spinal chord injury Stoke/CVA Post Polio Syndrome Spina Bifada Amputee Multiple Sclerosis Arthrogriposis Muscular Dystrophy Lower and upper extremity paralysis

The TiPower RimPower and PowerDrive X and SX are folding power/manual titanium wheelchairs.

The provided text describes the 510(k) summary for the TiPower RimPower and PowerDrive X & SX folding power/manual wheelchairs. This document focuses on demonstrating substantial equivalence to a predicate device (Commuter, K934232) and ensuring compliance with relevant standards, rather than detailing a clinical study with a device performance vs. acceptance criteria table, sample sizes, expert ground truth, or MRMC studies for AI devices.

Therefore, many of the requested elements for an AI device study are not applicable or cannot be extracted from this document, as it pertains to a physical medical device (wheelchair) and its compliance with established performance standards.

Here's a breakdown of what can be extracted based on the provided text:

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

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

This information is not provided in the document. The testing involved compliance with engineering standards, not clinical data sets with "test sets" in the context of AI or imaging devices.

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)

This information is not applicable to this type of device and testing. Ground truth in this context refers to compliance with established engineering and safety standards, not expert medical diagnoses.

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

This information is not applicable to this type of device and testing. Adjudication methods are typically used in clinical trials or AI model evaluation.

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

This information is not applicable as this is not an AI device or a diagnostic device involving human readers.

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

This information is not applicable as this is not an algorithm or AI device.

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

The "ground truth" for this device's performance is adherence to recognized international and national standards for wheelchairs (ISO 7176, ANSI/RESNA WC, EN 12184). These standards define objective performance and safety requirements.

8. The sample size for the training set

This information is not applicable as this is not an AI device with a "training set."

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

This information is not applicable as this is not an AI device with a "training set."

Ask a specific question about this device

Ask a specific question about this device