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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 ANY VIEW DR SERIES (Anyviw-500DR, Anyview-240DR) is radiation medical equipment only used by professional radiologists. This product is designed to provide fluoroscopic and spot film images of the patient during diagnostic and interventional procedures.

This system can be applied in emergency room, operation room, cast room or etc. of hospital.

The ANYVIEW DR SERIES (Anyviw-500DR, Anyview-320DR, Anyview-240DR) are mobile x-ray fluoroscopic imaging systems used by radiation experts. ANYVIEW DR SERIES are a digital fluoroscopic imaging systems with Flat Panel Detector (FPD) used in diagnostic and interventional procedures. ANYVIEW DR SERIES are composed of C-arm, x-ray generating equipment (x-ray controller, high voltage generator, x-ray tube, motor-type collimator), FPD, and workstation (console computer and monitor).

ANYVIEW imaging software is a Digital Imaging System (DIS) designed for C-arm, ANYVIEW FPD Fluoroscopic Mobile X-ray System. ANYVIEW imaging software provides useful functions to manage X-ray images obtained from ANYVIEW DR SERIES FPD Fluoroscopic Mobile X-ray System.

ANYVIEW imaging software provides various image tools. One of the most noticeable features is that the C-arm imaqes taken during an exam are stored in the database for further review. I Image data is integrated with the patient information in DICOM(OPTION) compatible format which allows compatibility with existing DICOM and PACS system.

The ECOTRON Co., Ltd. ANYVIEW DR SERIES FPD Fluoroscopic Mobile C-Arm (ANYVIEW-240DR, ANYVIEW-320DR, ANYVIEW-500DR) is a radiation medical equipment designed to provide fluoroscopic and spot film images for diagnostic and interventional procedures. The device was found to be substantially equivalent to its predicate device, Anyview-500R Fluoroscopic Mobile X-ray System (K160279), based on non-clinical performance data.

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not explicitly state formal "acceptance criteria" in a go/no-go format for performance metrics. Instead, it presents a comparison of technical characteristics and performance data of the subject device against its predicate and reference devices, demonstrating equivalent or improved performance without raising new safety or effectiveness concerns.

2. Sample size used for the test set and the data provenance:

• Test Set: The study primarily relies on bench testing and comparative performance data of components (specifically the Flat Panel Detector, FPD) for non-clinical evaluation. There is no mention of a traditional "test set" of patient images.
• Data Provenance: The data provenance for the performance characteristics (MTF, DQE, Spatial Resolution) of the detectors is from the respective manufacturers (VIEWORKS for Subject Device, TOSHIBA for Predicate Device, and Thales for Reference Device). This is non-clinical, technical performance data. The document does not specify country of origin for this component-level data, nor whether it is retrospective or prospective, though it is typically collected during product development/testing by the manufacturer.

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. The study is a 510(k) submission for substantial equivalence based on technical and performance characteristics of medical hardware, not on clinical image interpretation or diagnostic accuracy by experts. No ground truth was established by human experts for a test set of images.

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

Not applicable, as no test set requiring expert adjudication was used.

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 MRMC comparative effectiveness study was done. The device described is an X-ray imaging system, not an AI-powered image analysis tool.

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

Not applicable. The device is an imaging system, not an algorithm, and its performance evaluation does not involve standalone algorithm assessment.

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

Not applicable. The equivalence is established through engineering and technical performance comparisons of the device and its key components against predicate and reference devices, not against clinical ground truth. Performance metrics like MTF, DQE, and spatial resolution are derived from physical measurements and scientific testing of the imaging system and its components.

8. The sample size for the training set:

Not applicable. This is a medical device for imaging acquisition, not a machine learning or AI algorithm, so there is no concept of a "training set."

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

Not applicable.

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