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The PLX5200A is intended for use by a qualified/trained doctor or technologist on both adult and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. It is not intended for mammography.

The proposed device is a mobile x-ray system, it uses digital techniques for image capture, display and manipulation and the mobile design allows it to operate on mains or battery power and to be driven or pushed by an operator to various locations within a building or facility. It is commonly used for bedside imaging. This system consists of a mobile base unit, combined X-ray tube assembly, collimator, frame, X-ray flat panel detector, and image processing system.

This device is a Diagnostic X-ray System, which is a hardware device. The provided text indicates that no clinical study was included in this submission. Therefore, there is no acceptance criteria or study data for device performance as requested for AI/software devices.

However, the submission does mention various performance data related to the hardware itself, which are not based on clinical efficacy or diagnostic accuracy. These include:

• Electrical safety and electromagnetic compatibility (EMC): The system complies with IEC 60601-1:2005+A1:2012 / ANSI AAMI ES60601-1:2005/(R)2012 and A1:2012, IEC 60601-1-3:2008+A1:2013, IEC 60601-2-54:2009, IEC 62471 standard for safety, and IEC 60601-1-2: 2014 standard for EMC. Radio frequency (RF) wireless coexistence of equipment testing was performed according to IEEE ANSI C63.27-2017.
• Software Verification and Validation: The Embedded software and the workstation software were considered a "Moderate" level of concern and were verified and validated according to FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." They comply with FDA recognized standard IEC 62304.
• Cybersecurity: Documentation was provided according to "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices."
• DICOM: DICOM declaration was provided according to FDA recognized standard NEMA PS 3.1 - 3.20 (2016).
• Usability Testing: Usability validation was performed and complies with FDA recognized standard IEC 60601-1-6 Edition 3.1 and EC 62366-1 Edition 1.0.
• Risk Management: Activities were performed and documented according to FDA recognized standard ISO 14971 Second edition 2007-03-01.
• Biocompatibility Testing: Conducted in accordance with ISO 10993-1, including Cytotoxicity, Skin Sensitization, and Skin Irritation for the x-ray flat panel detector.

Since this is a hardware device clearance, and not an AI/software as a medical device (SaMD) or an AI-powered diagnostic device, the typical acceptance criteria and study design elements requested (like sample size for test/training sets, expert ground truth, MRMC studies, standalone performance) are not applicable or provided in this 510(k) summary.

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This software is intended to generate digital radiographic images of the skull, spinal column, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, or lying in the prone or supine position and is intended for use in all routine radiography exams. The product is not intended for mammographic applications.

This software is not meant for mammography, fluoroscopy, or angiography.

The I-Q View is a software package to be used with FDA cleared solid-state imaging receptors. It functions as a diagnostic x-ray image acquisition platform and allows these images to be transferred to hard copy, softcopy, and archive devices via DICOM protocol. The flat panel detector is not part of this submission. In the I-Q View software, the Digital Radiography Operator Console (DROC) software allows the following functions:

• 1. Add new patients to the system; enter information about the patient and physician that will be associated with the digital radiographic images.
• 2. Edit existing patient information.
• 3. Emergency registration and edit Emergency settings.
• 4. Pick from a selection of procedures, which defines the series of images to be acquired.
• 5. Adiust technique settings before capturing the x-ray image.
• 6. Preview the image, accept or reject the image entering comments or rejection reasons to the image. Accepted images will be sent to the selected output destinations.
• 7. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
• 8. Close a procedure when all images have been captured.
• 9. Review History images, resend and reprint images.
• 10. Re-exam a completed patient.
• 11. Protect patient records from being deleted by the system.
• 12. Delete an examined Study with all images being captured.
• 13. Edit User accounts.
• 14. Check statistical information.
• 15. Image QC.
• 16. Image stitching.
• 17. Provides electronic transfer of medical image data between medical devices.

The provided document is a 510(k) summary for the I-Q View software. It focuses on demonstrating substantial equivalence to a predicate device through bench testing and comparison of technical characteristics. It explicitly states that clinical testing was not required or performed.

Therefore, I cannot provide details on clinical acceptance criteria or a study proving the device meets them, as such a study was not conducted for this submission. The document relies on bench testing and comparison to a predicate device to establish substantial equivalence.

Here's a breakdown of what can be extracted from the provided text regarding acceptance criteria and the "study" (bench testing) that supports the device:

1. Table of Acceptance Criteria and Reported Device Performance

Since no clinical acceptance criteria or performance metrics are provided, this table will reflect the general statements made about the device performing to specifications.

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

• Test Set Sample Size: Not applicable/not reported. The document describes bench testing, not a test set of patient data.
• Data Provenance: Not applicable. Bench testing generally involves internal testing environments rather than patient data from specific countries or retrospective/prospective studies.

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

• Not applicable. As no clinical test set was used, no experts were needed to establish ground truth for patient data. Bench testing typically relies on engineering specifications and verification.

4. Adjudication method for the test set

• Not applicable. No clinical test set or human interpretation was involved.

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, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical Testing: The bench testing is significant enough to demonstrate that the I-Q View software is as good as the predicate software. All features and functionality have been tested and all specifications have been met. Therefore, it is our conclusion that clinical testing is not required to show substantial equivalence." The device is software for image acquisition, not an AI-assisted diagnostic tool.

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

• Yes, in a sense. The "study" described is bench testing of the software's functionality and its integration with solid-state detectors. This is an evaluation of the algorithm/software itself.

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

• For bench testing, the "ground truth" would be the engineering specifications and expected functional behavior of the software and its interaction with hardware components. It's about verifying that the software performs according to its design requirements.

8. The sample size for the training set

• Not applicable. The I-Q View is described as an image acquisition and processing software, not an AI/machine learning model that typically requires a training set of data.

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

• Not applicable, as there is no mention of a training set or AI/machine learning component.

Summary of the "Study" (Bench Testing) for K203703:

The "study" conducted for the I-Q View software was bench testing. This involved:

• Verification and validation of the software.
• Demonstrating the intended functions and relative performance of the software.
• Integration testing to verify that compatible solid-state detectors performed within specification as intended when used with the I-Q View software.

The conclusion drawn from this bench testing was that the software performs to specification and is "as safe and as functionally effective as the predicate software." This was deemed sufficient to demonstrate substantial equivalence, and clinical testing was explicitly stated as not required.

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Intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

Aquila 320-D Series: Small, light, easy handling. High Performance, High Power 320 mA, Leading Technology. Capacitive discharge technology, wireless image capture and transmission technology, smartphone image access. Wireless Detector and Workstation on touchscreen notebook or tablet. High frequency generator with micro-processor controls: Power 35.2 KW (The Aquila 320-S comes without the digital panels and workstation)

The provided text is a 510(k) summary for the AQUILA 320 D / AQUILA 320 S mobile x-ray system. It details the device, its intended use, and its substantial equivalence to a legally marketed predicate device. However, it does not describe an AI-powered device or a study where an AI device meets acceptance criteria.

The document explicitly states regarding clinical testing:
"Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance."

Therefore, I cannot provide the requested information about acceptance criteria and a study proving an AI device meets them based on the provided text, as this document pertains to a traditional medical imaging device (mobile x-ray system) and not an AI-powered one, and no clinical study was conducted or described.

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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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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

This represents the straightforward combination of three devices: One of three cleared MinXray Portable HF X-ray generators: a) HF120/60H PowerPlus cleared in K040046, (and in K141885) OR b) HF100H+ cleared in K052721 OR c) HF1202 PowerPlus cleared in K153059. Plus: A 510(k) cleared (K150929) Digital X-Ray Receptor Panel CareView 1500Cw X-ray Flat Panel Detector. d) e) PLUS: the dicomPACS® software package (Same as our predicate). The x-ray generators are portable units which operate from 120/240V 50-60° AC. The generator unit utilizes a high frequency inverter and can be mounted to a tripod or support arm. The usual safety precautions regarding the use of x-rays must be observed by the operator. The digital panel features the Careray flat panel technology in a sleek and compact unit. The portable panel provides digital X-ray image capture for a wide range of applications. The lightweight design, generous imaging area, and fast processing times of the detector make it easy to capture high quality diagnostic images for routine diagnosis, as well as challenging trauma and bedside exams. It's a portable solution for a faster, more streamlined approach to digital radiography. The only difference between this modified device and our predicate devices is the supplier of the digital x-ray receptor panel.

The provided text describes a 510(k) premarket notification for the MinXray CMDR 2CW (Multiple Models) mobile x-ray system. The submission aims to demonstrate substantial equivalence to a predicate device, the CMDR 2ST/CMDR 2SPE (Multiple Models).

Here's an analysis of the acceptance criteria and study information:

Acceptance Criteria and Reported Device Performance

The core of the acceptance criteria revolves around demonstrating substantial equivalence to the predicate device. This is primarily assessed by comparing the technological characteristics and showing that the new device is as safe and effective as the predicate, with the same indications for use.

The device performance is demonstrated through non-clinical testing, specifically focused on confirming proper system operation and diagnostic image quality.

The primary difference and therefore the key point of evaluation for substantial equivalence was the Digital X-ray Panel.

Study Information

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

   • The test set consisted of "several test exposures" using a radiographic phantom.
   • The data provenance is not explicitly stated in terms of country of origin, but it was generated during non-clinical bench testing by MinXray, Inc. This was a prospective test conducted for the purpose of this submission.

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

   • The text does not specify the number or qualifications of experts who evaluated the images during the bench testing. It only states that "The images were evaluated and found to be of diagnostic quality."

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

   • An adjudication method is not described. The evaluation was likely performed internally as part of the bench testing.

4. 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 or AI-assisted study was performed. The device is a mobile x-ray system, not an AI diagnostic tool.

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

   • Not applicable, as this is an x-ray imaging system, not a diagnostic algorithm.

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

   • The ground truth for the non-clinical testing was based on the expected diagnostic quality of images produced from a radiographic phantom, as assessed by comparison to images from the predicate device and general standards of diagnostic quality for x-ray imaging.

7. The sample size for the training set:

   • Not applicable, as this is not a machine learning device. The "training" here refers to the development and testing of the x-ray system components and their integration.

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

   • Not applicable in the context of a machine learning training set. For the development and verification of the x-ray system, the "ground truth" was established through engineering specifications, regulatory standards (e.g., UL, IEC, DHHS radiation safety), and the performance characteristics of previously cleared predicate/reference devices (generators, panels, software).

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These Portable Diagnostic Radiographic Systems are intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. (Not for mammography).

The AMADEO M-DR mini Portable X-Ray is a fully digital X-ray system for use for first aid services, intensive care units, emergency departments, as well as ships and mobile hospitals (e.g. container solutions) in inaccessible areas, in laboratories and scientific stations in remote parts of the world. The AMADEO M-DR mini is used for wireless digital X-ray imaging. The device represents the straightforward combination of 510(k) exempt devices (x-ray generator, code IZO and collimator, code IZX) and already cleared digital panels and imaging software. Two different models of digital image acquisition panels are offered: PerkinElmer XRpad2 4336 or CareView 1500CW. Both of the panels and the associated software have been previously cleared by FDA (K161966 or K150929). Integration with the portable system was straightforward.

The provided text is a 510(k) summary for the AMADEO M-DR mini and AMADEO M-AX mini mobile X-ray systems. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific device performance or clinical studies.

Therefore, many of the requested details about acceptance criteria, performance studies, sample sizes, expert qualifications, and ground truth establishment are not present in this document.

Here's a breakdown of what can be extracted and what is missing:

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

The document doesn't provide a table of formal acceptance criteria with numerical performance targets for the device. Instead, it asserts equivalence to the predicate device based on meeting recognized standards and similar characteristics.

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

• Sample Size: Not specified. The document mentions "test images" were obtained, but no specific number of images or patients is provided.
• Data Provenance: Not specified. As clinical testing was not required, there is no mention of country of origin or whether data was retrospective or prospective.

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

This information is not provided. Since the submission states clinical testing was "Not required because the proposed digital panels have prior FDA clearance," it implies that new ground truth establishment by experts for image interpretation was not part of this specific submission.

4. Adjudication method for the test set:

Not applicable, as no formal clinical test set or adjudication by experts is described.

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 submission is for a mobile X-ray system, not an AI-powered diagnostic device, and therefore, an MRMC study comparing human readers with and without AI assistance was not performed or mentioned.

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

Not applicable. This device is an imaging system, not a standalone algorithm.

7. The type of ground truth used:

The concept of "ground truth" in the context of diagnostic accuracy from expert consensus or pathology is not directly applicable to this submission. The "ground truth" for this device's performance relies on its ability to produce diagnostic radiographic exposures that are comparable to those of the predicate device and meet established safety and performance standards. This is evaluated through:

• Compliance with DHHS Radiation Safety Performance Standard.
• Compliance with electrical safety and EMC standards (IEC-60601 series).
• Test images demonstrating total system functionality.

8. The sample size for the training set:

Not applicable. This is not an AI/machine learning device that requires a training set.

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

Not applicable.

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The CareView 1500P detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This product is not intended for mammography applications.

CareView 1500P is a kind of portable wireless digital X-ray flat panel detectors which have 434mm×355mm imaging area. The device communicates by not only the wireless communication but also wired communication feature (Giga-bit Ethernet communication mode by backup network port) optionally.

The device intercepts X-ray photons and then the scintillator emits visible spectrum photons that illuminate an array of photo detectors (a-Si) that create electrical signals. After the electrical signals are generated, it is converted to a digital value and an image will be displayed on the monitor.

The detector should be integrated with an operating PC and an X-ray generator to utilize as digitalizing X-ray images and transfer for radiography diagnostic.

The provided text describes a 510(k) premarket notification for the CareView 1500P X-ray Flat Panel Detectors. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study to prove meeting acceptance criteria in the context of an AI/algorithm-based device.

Therefore, many of the requested categories (e.g., sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance, ground truth types) are not applicable or cannot be extracted from this document, as it pertains to an X-ray detector itself, not an AI algorithm.

However, I can extract information related to the device's technical specifications and the comparison made for substantial equivalence.

Here's a summary based on the provided text, indicating when information is not applicable (N/A) or not provided (NP):

1. Table of Acceptance Criteria and Reported Device Performance

The document directly compares the proposed device (CareView 1500P) with its predicate device (CareView 1500Cw) to demonstrate substantial equivalence, rather than listing specific "acceptance criteria" in the sense of performance thresholds for an AI/algorithm. The performance specifications are presented as direct comparisons.

Study Proving Device Meets Acceptance Criteria:

The document states that Electrical safety and EMC testing according to IEC/ES 60601-1 and IEC/EN 60601-1-2 was performed, and "All test results are satisfactory."

For performance, the document relies on a comparison to the predicate device, CareView 1500Cw (K150929). It argues that the proposed device, CareView 1500P, maintains similar technological characteristics and performance (e.g., MTF, DQE, imaging specifications) to the cleared predicate, and that the differences in mechanical structure and power consumption do not raise new questions of safety and effectiveness.

2. Sample size used for the test set and the data provenance: Not applicable (N/A) – this is a hardware device clearance, not an AI algorithm. No clinical test set data is described in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: N/A - no test set involving expert ground truth is described.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: N/A - no test set described.

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: N/A - this is a hardware device, not an AI product.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: N/A - this is a hardware device, not an AI product.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): N/A - no ground truth in the context of an AI algorithm is mentioned. The ground truth for the device's technical specifications would be engineering measurements and adherence to standards.

8. The sample size for the training set: N/A - no AI training set is described.

9. How the ground truth for the training set was established: N/A - no AI training set is described.

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