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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 physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

RadmediX AcuityPDR combines components into a complete mobile x-ray system, including software, a generator/collimator combination, and digital radiography panels. Radmedix combines components from various manufacturers into a complete mobile x-ray system. The customer selects one (or more) of the following digital x-ray receptor panels: DRTech 4343A, (K192400); DRTech 4343W, (K193017); AcuityDR (K171137); AcuityDR 1417 (K162552 EVS 3643, EVS 3643G) or AcuityDR 1717 (K162555 EVS 4343, EVS 4343G). In addition, the customer selects one of three software packages: Accuvue+ (K130883), AccuVueMED, (K152172) or AccuVue (K141440). The generator can be battery operated. A single battery charge produces 200 Exposures at max KV and MAS settings. The battery charger is UL Listed and the internal lithium ion battery is overcharge and overcurrent protected. A typical acquisition computer would be a Lenovo P53S or a Dell Precision 3541.

This document describes a medical device, AcuityPDR, a mobile X-ray system, and its 510(k) submission for FDA clearance. The submission relies on demonstrating substantial equivalence to a predicate device (MinXray CMDR 2CW).

Here's an analysis of the requested information based on the provided text:

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

The document does not explicitly state acceptance criteria in the form of quantitative performance metrics for the AcuityPDR device itself. Instead, it focuses on demonstrating "substantial equivalence" to a predicate device. The acceptance criteria for this type of submission are typically that the device performs as safely and effectively as the predicate and has the same indications for use.

The device performance is reported implicitly through the comparison table on page 4, showing that the AcuityPDR either matches or has comparable/equivalent functionality to the predicate in various technical parameters.

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

The document explicitly states in section 6, "Clinical testing. Not required for a determination of substantial equivalence." This indicates that no clinical test set was used for this specific submission. The submission relies on non-clinical testing (bench testing, standards compliance) and comparison to the predicate device.

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)

Since no clinical test set was required or used for this submission, there is no mention of experts being used to establish ground truth for a test set.

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

As no clinical test set was used, no adjudication method is mentioned.

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 study was performed as clinical testing was "Not required for a determination of substantial equivalence." The device is a mobile X-ray system, not an AI-powered diagnostic tool, so the concept of human readers improving with AI assistance is not applicable in this context.

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

This is not an AI algorithm; it's a mobile X-ray system. Therefore, standalone algorithm performance is not applicable. The performance is assessed through compliance with electrical and safety standards for X-ray devices and comparison of technical specifications to a predicate.

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

For this type of 510(k) submission, "ground truth" is primarily established by:

• Compliance with recognized electrical, safety, and performance standards for X-ray generating equipment (e.g., IEC 60601 series, 21 CFR 1020).
• Demonstration that the device's technical specifications and intended use are substantially equivalent to a legally marketed predicate device.
• Component certifications: "All of the components subject to the CDRH performance standard are certified to comply with the standard by their respective manufacturers."

8. The sample size for the training set

The document does not describe a training set for an AI model, as this is not an AI device. The "training" for such a device would refer to its design and manufacturing processes adhering to established engineering principles and standards.

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

Not applicable, as no AI model or training set is described. The "ground truth" for the device's design and manufacturing relies on established medical device regulations, design controls, and industry standards for X-ray imaging equipment.

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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 not intended for mammography applications. This device is intended for use by qualified medical personnel and is contraindicated when, in the physician, procedures would be contrary to the best interest of the patient.

Intended for digital image capture use in general radiographic examinations, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. The kit allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis, and extremities.

This is a Windows 10 based software to be used in conjunction with an FDA cleared digital x-ray receptor panel. It can be used to upgrade film-based systems. This upgrade allows to acquire digital medical diagnostic X-ray images and transfer the images to hardcopy, softcopy, and archive devices on the same network. Some functions allowed with the E-COM DR-2000 DR software:
a. Add new patients to the system, enter information about the patient and physician that will be associated with the digital radiographic images.
b. Edit existing patient information.
c. Emergency registration and edit Emergency settings.
d. Pick from a selection of procedures, which defines the series of images to be taken.
e. Adjust technique settings before capturing the X-ray image.
f. 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.
g. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
h. Close a procedure when all images have been captured.
i. Review History images, resend and reprint images.
j. Re-exam a completed patient.
k. Protect patient records from being deleted by the system.
l. Delete an examined Study with all images being captured.
m. Edit user accounts.
n. Check statistical information.
o. Image QC.
p. Image stitching.

This document describes the FDA 510(k) premarket notification for the E-COM DR-2000 DR, a stationary x-ray system. However, it does not provide details regarding specific acceptance criteria, a study proving the device meets those criteria, or information on AI/standalone performance, expert adjudication, or ground truth establishment typically associated with such studies.

Here's a breakdown of the requested information based only on the provided text, highlighting what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting specific quantitative performance metrics against acceptance criteria.

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided in the document. No specific test set for performance evaluation is mentioned. The submission states "Clinical Testing: Not required for a showing of substantial equivalence," implying a lack of a dedicated clinical test set for new performance evaluation.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the document. Since no clinical testing or performance study is detailed, there's no mention of experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set

This information is not provided in the document. As no test set is described, no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

This information is not provided in the document. The device is a "Stationary X-ray System" with software for image acquisition and management. There is no indication that it includes AI for interpreting images or assisting human readers. Therefore, an MRMC study related to AI assistance is not applicable and not mentioned.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This information is not provided in the document. As there is no mention of an algorithm for image interpretation or analysis, a standalone performance study is not applicable and not mentioned. The device's software functions are primarily for image acquisition, processing, and management.

7. The Type of Ground Truth Used

This information is not provided in the document. Since no clinical performance study is described, there's no mention of the type of ground truth used (e.g., expert consensus, pathology, outcomes data).

8. The Sample Size for the Training Set

This information is not provided in the document. The submission pertains to a conventional x-ray system and its control software. There is no discussion of machine learning or AI components that would require a "training set."

9. How the Ground Truth for the Training Set Was Established

This information is not provided in the document. As no training set is discussed (see point 8), the establishment of its ground truth is also not mentioned.

Summary of the Document's Content Regarding Testing:

The K193644 submission's primary focus is on demonstrating substantial equivalence to a predicate device (K130883, Sedecal Digital Radiographic Upgrade Model SDRU-T). This is achieved by comparing the new device's indications for use and technological characteristics to the predicate.

The document states:

• "Bench/Performance Testing: The results of bench testing (software validation and risk analysis for a moderate level of concern device) shows that this new device poses no new issues of safety or effectiveness, has essentially the same technological characteristics as the predicate, and is therefore substantially equivalent to the predicate device."
• "Clinical Testing: Not required for a showing of substantial equivalence."

This indicates that the FDA clearance for E-COM DR-2000 DR was based on demonstrating similar technical specifications and safety/effectiveness profiles to an already cleared device, along with adherence to relevant software development and risk management guidance documents. It does not involve a new performance study with specific quantitative acceptance criteria or extensive clinical data as would be required for a novel device or one incorporating advanced AI algorithms.

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The purpose of Acuity is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, angiography, interventional, or fluoroscopy use.

RadmediX Acuity combines components into a complete stationary x-ray system, including software, tube stands, tube heads, collimators, generators, tables, and digital radiography panels.

Here's a breakdown of the acceptance criteria and the study information based on the provided text:

Acceptance Criteria and Device Performance:

The document describes the Acuity SDR Standard, Acuity SDR Plus, and Acuity FDR Standard as stationary x-ray systems. The core claim for substantial equivalence is that the device performs the same functions using the same technological methods as the predicate device (Visaris Vision®) to produce diagnostic x-ray images.

Study Information:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a numerical sample size of clinical cases for a formal clinical trial. Instead, the non-clinical testing involved acquiring DICOM images from "all major body structures" using one AcuitySDR system configuration.
   • Data Provenance: The document does not specify the country of origin for the image acquisition or whether it was retrospective or prospective. It was a "field tested" system, implying real-world or simulated real-world scenarios.

2. Number of Experts and Qualifications (for Ground Truth):

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified, beyond the statement that images were generated for "diagnostic x-rays" and assessed as "clinically acceptable." This implies evaluation by trained personnel (e.g., radiologic technologists, radiologists), but their specific qualifications or number are not detailed.

3. Adjudication Method:

   • No formal adjudication method is described. The assessment of image quality was based on whether images were "of high quality and contrast and clinically acceptable."

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC comparative effectiveness study was performed or described. The submission focuses on demonstrating substantial equivalence through technical characteristics and non-clinical image quality assessment, not on human reader performance improvement with or without AI assistance.

5. Standalone Performance Study (Algorithm Only):

   • Yes, a standalone study (non-clinical testing) was performed. The algorithm (software) alongside the hardware components was tested to acquire and process images. The output (DICOM images) was then evaluated for inherent quality and acceptability. The assessment "all images were of high quality and contrast and clinically acceptable" refers to the output of the integrated system.

6. Type of Ground Truth Used:

   • Expert Consensus / Clinical Acceptability: The ground truth for image quality was established through subjective expert assessment of "high quality and contrast and clinically acceptable." It does not mention pathology or outcomes data.

7. Training Set Sample Size:

   • Not applicable. This device is an X-ray system, not an AI/ML algorithm that requires a training set in the typical sense for image interpretation or diagnosis. The "software" components mentioned (AccuVueMED, AccuVue) are image acquisition and processing software, which are generally deterministic and not "trained" on data in the way a diagnostic AI would be. They are "previously cleared software supplied with the system."

8. How Ground Truth for Training Set Was Established:

   • Not applicable, as there is no specific "training set" for an AI/ML algorithm in this submission. The software components were previously cleared by the FDA, implying their performance was established through other means at the time of their original clearance (K152172 and K141440).

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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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Intended for use by a qualified trained physician 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: HF120/60H PowerPlus cleared in K040046, (and in K141885) OR HF100H+ cleared in K052721 OR HF1202 PowerPlus cleared in K153059. One of three cleared digital X-ray receptor panels: Toshiba FDX3543RP OR the Toshiba FDX3543RPW cleared in K162687 (and others) OR the PerkinElmer Solid State Imager, (K140551) PLUS: the dicomPACS® software package (K141885) (Same as our predicate). The X-ray generators are portable units which operate from 120 V 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 formerly used Toshiba panels or PerkinElmer flat panel technology in a sleek and compact unit. The portable panels provide 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 device is the digital x-ray receptor panel.

The provided text does not contain information about acceptance criteria and a study proving a device meets these criteria in the typical sense of a diagnostic medical device evaluating patient data for specific clinical endpoints.

Instead, the document is a 510(k) premarket notification for a mobile X-ray system, which is a piece of medical imaging equipment. The "acceptance criteria" and "study" described herein relate to demonstrating substantial equivalence to a previously cleared predicate device, rather than proving the performance of a diagnostic algorithm against a clinical ground truth.

Here's an analysis based on the provided text, addressing the points where information is available:

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

The document doesn't present a formal table of acceptance criteria for a diagnostic performance study. Instead, it focuses on demonstrating that the new devices have similar technological characteristics and performance to the predicate device, and that they produce images of "diagnostic quality."

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

• Test Set Description: The "test set" in this context refers to prototype systems and phantom images, not a clinical image dataset with patient outcomes.
• Sample Size: "Prototype systems covering all generator/panel combinations were assembled and tested." "Several test exposures showed that the system was operating properly." "All panel/generator combinations were tested" with the DIGI-13 device. The exact number of exposures or phantom images is not specified beyond "several" and "all combinations."
• Data Provenance: This was bench testing performed internally by MinXray, Inc. The data is entirely synthetic (phantom images) and technical system performance data, not patient data from a specific country or for retrospective/prospective analysis.

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

• Number of Experts: Not specified. The evaluation of "diagnostic quality" from the phantom images was an internal assessment.
• Qualifications of Experts: Not specified. It's implied that the evaluation was done by the manufacturer's personnel, likely engineers or qualified technicians, as part of the system testing. There is no mention of external radiologists or clinicians establishing ground truth for these technical images.

4. Adjudication method for the test set:

• Adjudication Method: Not applicable. There was no multi-reader or consensus-based adjudication in a clinical diagnostic sense. The evaluation was a technical assessment of image quality and functionality against established safety and performance standards for X-ray equipment.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is a mobile X-ray system, not an AI-powered diagnostic tool.
• Effect Size of AI Assistance: Not applicable, as no AI component or human-in-the-loop diagnostic performance was evaluated.

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

• Standalone Performance: Not applicable. This device is a hardware system for acquiring X-ray images, not a standalone diagnostic algorithm.

7. The type of ground truth used:

• Type of Ground Truth: The "ground truth" here is fundamentally technical adherence to performance standards and comparison to a predicate device's established image quality using a phantom. It's not clinical diagnosis, pathology, or outcomes data. The i.b.a. Test Device DIGI-13 (a device for quality tests at CR and DR systems) served as a standard for image quality assessment.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. This is not an AI/machine learning device. There is no mention of a "training set" in the context of diagnostic algorithms.

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

• Ground Truth Establishment for Training Set: Not applicable. As there is no training set for a diagnostic algorithm, there's no ground truth established in that context.

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The ArtPix Mobile EZ2GO, is intended for use in general radiographic examinations by qualified/trained doctors or technicians, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography.

ArtPix Mobile EZ2GO allows imaging of the skull, chest, shoulders, spine, abdomen, and extremities. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position.

The modified ArtPix Mobile EZ2GO is a flexible high-resolution Digital Radiography (DR) system that is designed to provide fast and smooth radiography examinations of sitting, standing or lying patient Imaging. The modified ArtPix Mobile EZ2GO consists of the following components: Tablet Computer, Flat Panel Detectors, Detector Removable batteries & Charger, and other optional components such as External Monitor for Docking Station, USB HUB, Bluetooth DAP device and Fix Room Access Point Support. Images may be transferred via a network using DICOM protocol for printing and storage in PACS.

The modified ArtPix Mobile EZ2GO can be used in combination with any conventional analog radiography system currently using film screen or CR, fixed or mobile, or other system capable of producing general radiography exposures.

The modified Artpix Mobile EZ2GO detector can be shared between multiple Artpix Mobile EZ2GO systems.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text.

Based on the provided document, the device described is ArtPix Mobile EZ2GO, an X-ray system. The document is a 510(k) summary for a Special 510(k) submission, indicating modifications to a previously cleared predicate device, PrestoDR 4143 [K110849](https://510k.innolitics.com/search/K110849).

It's important to note that this document is a 510(k) submission summary, which primarily aims to demonstrate substantial equivalence to a predicate device rather than providing a detailed report of a new clinical trial. Therefore, information regarding certain aspects like "sample size used for the test set," "number of experts used to establish ground truth," "adjudication method," and "MRMC comparative effectiveness study" are not typically part of a 510(k) summary for this type of device and are not present in the provided text. These elements are more common for AI/CADe devices or those requiring de novo classification with novel claims.

The "study" proving the device meets the acceptance criteria is primarily a verification and validation (V&V) process focused on demonstrating that the modified device's performance is equivalent to or better than the predicate device and that it meets established engineering and functional requirements, without raising new safety or effectiveness concerns.

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

The document focuses on comparing the modified device (ArtPix Mobile EZ2GO) to its predicate (PrestoDR 4143 [K110849](https://510k.innolitics.com/search/K110849)) across various technical specifications and performance characteristics rather than explicitly stating acceptance criteria in table form for each parameter and then listing the observed performance. Instead, it provides a comparative table and a general conclusion of acceptance.

However, we can infer some "acceptance criteria" from the comparison table (Section 1.9.2) and the V&V section (1.10). The primary acceptance criterion for this 510(k) is substantial equivalence to the predicate device in terms of fundamental scientific technology, indication for use, safety, and effectiveness.

Here's an inferred table based on the provided comparison:

Conclusion from the document: "The modified ArtPix Mobile EZ2GO system passed its acceptance criteria and is recommended for release."

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

The document does not specify a "sample size" in terms of patient data or images for testing the device's diagnostic performance, as it is a technical modification to an existing X-ray system rather than a new AI/CAD device. The "test set" in this context refers to the device itself being subjected to engineering verification and validation.

• Sample Size for Test Set: Not applicable in the context of patient data or clinical images. The testing refers to the system hardware and software.
• Data Provenance: Not applicable. The V&V focused on the device's technical specifications and functionality.

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 provided and is generally not required for a 510(k) summary concerning technical modifications to an X-ray imaging system. Establishing ground truth by experts is relevant for assessing diagnostic accuracy, which is not the primary focus of this type of submission (since the diagnostic capability relies on the predicate's established performance and the fundamental imaging physics).

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

This information is not provided and is not typically relevant for this type of 510(k) submission. Adjudication methods are used in clinical trials, particularly for diagnostic algorithms, where there might be disagreement among reviewers regarding the ground truth.

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 or reported. This type of study is specific to devices, especially AI/CAD, that assist human readers in making diagnoses. The ArtPix Mobile EZ2GO is an X-ray imaging system itself, not an AI software. The submission focuses on demonstrating that the new hardware and software components maintain diagnostic image quality and system functionality equivalent to the predicate.

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

No standalone algorithm performance study was done or reported. The device is an integral imaging system requiring human operation (qualified/trained doctors or technicians). The submission does not claim any autonomous algorithmic diagnostic capabilities.

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

Since this is not a diagnostic AI/CAD device but an imaging system, the "ground truth" concept in the traditional sense (e.g., pathology for disease detection) is not directly applicable to the V&V described. The V&V focused on:

• Expected performance of software functions ("pass/fail" based on test protocols).
• Correctness of system integration.
• Measured image quality metrics (DQE, MTF) against expected engineering specifications which are themselves proxies for producing diagnostic quality images.
• Adherence to safety and electrical standards.

So, the "ground truth" essentially refers to engineered specifications and functional requirements.

8. The sample size for the training set

Not applicable. The ArtPix Mobile EZ2GO is an X-ray imaging device, not an AI/machine learning algorithm that requires a training set of data.

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

Not applicable. (See point 8).

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The D-RS AT Digital Dynamic Remote System Is indicated for use in generating fluoroscopic images of human anatomy for diagnostic procedures. It Is intended to replace fluoroscopic images obtained through image intensionaly. Not intended for mammography applications.

The D2RS AT Digital Dynamic Remote System is a direct digital dynamic remote-controlled fluoroscopy and radiography system equipped with the latest generation of Trixell Flat Panel Detector (FPD). The single FPD can perform both fluoroscopy and radiography and is detachable and portable for direct projections to create a unique and highly versatile 3-in-1 imaging solution. The receptor panel directly converts the X-ray images captured by the sensor into a high-resolution digital images. The instrument is suited for use inside a patient environment. This unit converts the X-rays into digital signals. The unit can acquire still and moving images. The system includes a remotely controlled tilting/elevating table. Panel information: Based on a flat-panel digital detector with the largest field-of-view in the market, the Pixium RF 4343 FL is designed for easy integration in classical R&F tables. It allows manufacturers to offer radiologists an all-digital real-time system, generating high-quality images for both routine dynamic applications and high-end static ones. Digital technology facilitates the work of X-ray technicians and speeds up the process, allowing medical centers to optimize patient throughput. Universal: a single detector for both Radiography and Fluoroscopy. An optional static panel may be added to the system: Trixell Pixium 3543EZ. The 3543EZ panel has already been cleared in various 510(k)s including K131314 and K141440. That panel is not mounted and can be moved around by the technician. The system must always have either the Pixium RF 4343, Pixium RF 4343FL for the system to be able to perform fluoroscopy. These fluoroscopic panels were not to our knowledge previously cleared by FDA, so complete performance information as requested by the FDA guidance document on solid state x-ray panels has been provided.

This document describes the D²RS_AT Digital Dynamic Remote System, an image-intensified fluoroscopic x-ray system seeking 510(k) clearance. The provided text outlines the device's characteristics, its comparison to a predicate device, and the testing conducted to demonstrate its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document implicitly defines acceptance criteria through comparison to a predicate device (Stephanix D²RF K102529) and adherence to various standards and guidance documents. Explicit quantitative acceptance criteria are not provided as a list with pass/fail thresholds. Instead, performance metrics are presented for both the new device's panels and the predicate device's panel, and the conclusion states that the new device performs "comparably to and is substantially equivalent to the predicate device."

Study Proving Acceptance Criteria:

The study conducted to prove the device meets the acceptance criteria is detailed under "Bench/Performance Testing/Data" and "Clinical Evaluation."

2. Sample Size Used for the Test Set and Data Provenance:

• Test Set Sample Size: Not explicitly stated. The "Clinical Evaluation" mentions a "board certified radiologist reviewed both static and moving images." This implies a qualitative assessment of some images, but the number of images or cases reviewed is not specified.
• Data Provenance: The document does not specify country of origin for the data or whether it was retrospective or prospective. Given that the device is manufactured in France (Rue Jean Moulin - ZI du Bayon La Ricamarie - FRANCE), it's plausible testing was conducted there or in another location appropriate for device evaluation. The context of a 510(k) submission typically involves data collected specifically for the regulatory submission, making it akin to a prospective study for that purpose, even if the cases themselves might be simulated or representative.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• Number of Experts: "a board certified radiologist reviewed..." indicates one radiologist.
• Qualifications of Experts: "board certified radiologist." Specific experience (e.g., "10 years of experience") is not provided.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable or "none" for multiple readers. Since only one radiologist is mentioned as reviewing the images, there was no multi-reader consensus or adjudication process described. The single radiologist's assessment served as the "truth" for the clinical evaluation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC comparative effectiveness study was not explicitly described. The "Clinical Evaluation" section mentions a single board-certified radiologist reviewing images but does not present a comparison of human reader performance with and without AI assistance, nor does it quantify an effect size of improvement. The study focuses on the device's image quality for diagnostic purposes rather than the reader's comparative performance with different aids.

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

• Yes, a standalone study (or rather, non-human-in-the-loop performance evaluation) was done as part of the bench testing. The "Bench/Performance Testing/Data" section discusses objective metrics like MTF, DQE, spatial resolution, and pixel pitch. These are measurements of the device's inherent image quality characteristics, independent of a human observer's interpretation.

7. The Type of Ground Truth Used:

• For Bench/Performance Testing: The ground truth is objective physical measurements of the system's performance characteristics (e.g., specific X-ray phantom designs for MTF/DQE, calibrated measurement tools for spatial resolution, etc.).
• For Clinical Evaluation: The "ground truth" was established by the subjective assessment of "good diagnostic quality" by a single board certified radiologist. This is an expert opinion-based ground truth.

8. The Sample Size for the Training Set:

• Not Applicable / Not Provided. This device is an X-ray imaging system, not an AI / machine learning algorithm that typically requires a training set of data. The document describes a physical medical imaging device and its components (flat panel detectors). Therefore, no "training set" in the context of machine learning is applicable or mentioned.

9. How the Ground Truth for the Training Set Was Established:

• Not Applicable / Not Provided. As there is no mention of an AI/ML component requiring a training set, the establishment of ground truth for such a set is not relevant to this submission.

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