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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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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatic patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone/ supine position.

The DigiX FDX System is not meant for mammography.

The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic X-Ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaqing.

The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted X-Ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, X-Ray generator, X-Ray tube, beam limiting device, and a solid-state image receptor.

The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on demonstrating substantial equivalence to predicate devices rather than defining specific quantitative performance metrics as "acceptance criteria" for a new AI/software feature in the way clinical performance studies usually do. Instead, the "acceptance criteria" are implied to be that the modifications do not negatively impact safety or effectiveness, and that the device performs comparably to the predicate devices and meets relevant safety standards.

For the new features explicitly mentioned (Automatic Stitching and Dual Energy Subtraction), the document states they add functionality without affecting patient safety or effectiveness. For the other components, the criteria are often "Same" or "Similar functionality with same imaging results" or "doesn't affect the safety or effectiveness."

To represent this in a table, we'll extract performance comparisons from the "Functional and Specification Differences" table (Table 4) and the "Justification for Differences" (Table 5).

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

The document states:

• "Non-clinical testing included verification and validation testing, image evaluation, testing, and safety testing."
• "Performance testing included functional testing of all motions of the system(s) with respect to the design specifications. Image performance testing was conducted and results included in the submission."
• "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system."

The document does NOT specify a sample size for any test set (clinical or otherwise) in terms of patient data or images used for validation of the radiographic system itself, nor does it mention data provenance (country of origin, retrospective/prospective). It primarily relies on hardware and software equivalence, and compliance with industry standards.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. The document explicitly states "Clinical testing is not applicable." Therefore, there was no clinical study conducted that would necessitate expert readers to establish ground truth for a test set. The evaluation focuses on technical performance and equivalence to predicate devices.

4. Adjudication Method for the Test Set

Not applicable. As no clinical test set requiring expert adjudication was performed, no adjudication method is mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was done. The document states "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system." Therefore, there is no information on how much human readers improve with or without AI assistance. The new software features (Automatic Stitching, Dual Energy Subtraction) are presented as additional functionalities that don't affect safety or effectiveness, not as AI-assisted diagnostic tools requiring a comparative effectiveness study.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Not applicable. The device is a radiographic system, not an AI algorithm intended for standalone diagnostic performance. While it includes new software features (Automatic Stitching, Dual Energy Subtraction), these are integrated functionalities of the imaging system and not described as standalone diagnostic algorithms requiring independent performance evaluation without human interaction.

7. Type of Ground Truth Used

The "ground truth" for the evaluation is primarily based on:

• Compliance with technical specifications and design requirements: Functional testing, image performance testing.
• Adherence to safety and performance standards: IEC 60601 series, EN ISO 14971, IEC 62366-1, IEC 62304, 21 CFR 1020.30, 21 CFR 1020.31.
• Substantial equivalence to predicate devices: Demonstration that the new device has the same intended use, fundamental technological characteristics, and that any differences do not raise new questions of safety or effectiveness.
• Software documentation assessment: For the software components, including the new features, documentation was provided for a "Moderate Level of Concern" software as per FDA guidance.

No clinical ground truth (e.g., expert consensus, pathology, outcomes data) was used or required given the nature of this submission.

8. Sample Size for the Training Set

Not applicable. The document describes an X-ray imaging system, not an AI/ML device that requires a training set of data. The new software features (Automatic Stitching, Dual Energy Subtraction) are described as functionalities, not adaptive algorithms that learn from data.

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

Not applicable. As there was no training set, there was no ground truth to establish for it.

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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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ProRad Series Stationary Radiographic System is intended for use by a qualified, trained doctor or technician on both adult and paediatric 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 ProRad series Stationary Radiographic System is a diagnostic x-ray system intended for general purpose radiographic imaging of the human body. There are two types of configurations (2FC and 3NC) for ProRad; the difference is in the mounting of the X-ray tube. For X-ray tube mounting the configuration is either the floor mounted (2FC) or ceiling suspension (3NC) assembly.

The devices are a new combination of a previously cleared solid state digital x-ray acquisition panel and software with the diagnostic x-ray components (including Xray tube, high frequency X-ray generator, a tilting vertical bucky, X-ray table and collimator) required to make a complete system. The purchaser may select any of the digital panels and software based on the user's requirements. The other components are also available in different configurations to meet specific customer needs. The X-ray panel and imaging software have been previously cleared by the FDA, and most of the other components are used in previously cleared 510(k) devices.

Here's an analysis of the acceptance criteria and study information for the ProRad 2FC and ProRad 3NC Digital Stationary Radiographic Systems, based on the provided text:

Acceptance Criteria and Device Performance Table:

The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than setting and reporting specific performance metrics with acceptance criteria in a comparative table for novel device features. The "Comparable Properties" table (pages 6-8) is used to show alignment with predicate devices.

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

• Sample Size: Not explicitly stated as a separate "test set" for a dedicated algorithm performance study. Instead, compliance is demonstrated through testing of the integrated system and reliance on previous FDA clearances for individual components.
• Data Provenance: Not applicable in the context of an algorithm performance test set. The clinical images reviewed by a radiologist were "acquired by the device," but the origin (e.g., country, prospective/retrospective status) is not specified.

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

• Number of Experts: One radiologist.
• Qualifications of Experts: Only "a radiologist" is mentioned. Specific qualifications (e.g., years of experience, board certification) are not detailed.

3. Adjudication method for the test set:

• Adjudication method: Not applicable. The radiologist's review was a single assessment, not a consensus or adjudication process among multiple readers.

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:

• MRMC Study: No. The document explicitly states: "Since the digital x-ray panels and software have previously received FDA clearance, a clinical study was not required as per the FDA guidance document." The clinical image review was supplementary.
• Effect size of human reader improvement with AI: Not applicable, as no MRMC study or AI assistance evaluation was conducted. The device is an imaging system, not an AI diagnostic tool.

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

• Standalone Performance Study: No. This device is a diagnostic X-ray system, which intrinsically requires a human (a qualified doctor or technician) in the loop for operation and interpretation. The performance of individual cleared components (digital panels, software) was relied upon.

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

• Type of Ground Truth: For the supplementary review, the "ground truth" was the radiologist's assessment that the images were "acceptable and allowed the radiologist to make an accurate diagnosis." This is a form of expert opinion on image quality and diagnostic utility, rather than an objective "truth" like pathology or outcomes.

7. The sample size for the training set:

• Sample Size for Training Set: Not applicable. This document does not describe a machine learning algorithm that requires a training set. The device is a conventional X-ray system composed of cleared components.

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

• Ground Truth for Training Set: Not applicable, as there is no machine learning algorithm described.

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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 DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone position. The DigiX FDX System is not meant for mammography. The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic x-ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaging. The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted x-ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, x-ray generator, x-ray tube, beam limiting device, and a solid-state image receptor. The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

Here's a summary of the acceptance criteria and the study information for the Allengers Medical Systems Limited DigiX FDX device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on substantial equivalence to a predicate device (Siemens Ysio K081722) rather than defining explicit, quantitative acceptance criteria for clinical performance. The "acceptance criteria" can be inferred as meeting or being sufficiently similar to the predicate device in terms of functionality and safety, as well as complying with relevant standards.

• G1092: 0.6mm/1.2mm focal spot (Predicate: 0.6mm/1.0mm), 1 MHU (Predicate: 783 kHU), 108 mm target diameter (Predicate: 100 mm).
  G292: 0.6mm/1.2mm focal spot (Predicate: 0.6mm/1.0mm), 600 KHU (Same), 12° target angle (Predicate: 16°), 102 mm target diameter (Predicate: 100 mm). | Similar, providing essentially same imaging resolution and higher loading for some G1092 models. Different target angle for G292 provides full coverage at 40" SID. Differences in heat units and target diameter also noted as "similar" or providing higher loading/instantaneous focal spot loading. |
  | Beam Limiting Device: Construction, compliance with CFR 21 1020.31, automatic feature. | Matches predicate. | Same. |
  | Solid State X-ray Image Detectors: Panel type, active area, pixel pitch, pixel matrix, input scintillator, limiting resolution. | Detectors (e.g., P-E XRPAD 4343F vs. Trixell Pixium 4600):
• Active area: 432x432mm (Predicate: 429x429mm).
• Pixel pitch: 100 µm (Predicate: 143 µm).
• Pixel matrix: 4318x4320 (Predicate: 3001x3001).
• Limiting resolution: 5 lp/mm (Predicate: 3.57 lp/mm).
  Similar differences for other detector models listed. | Essentially the same imaging area. Provides higher resolution for pixel pitch, pixel matrix, and limiting resolution in most cases, which is considered an improvement and not negatively affecting safety or effectiveness. |
  | Viewing Monitors: Size, resolution. | Matches predicate. | Same. |
  | Software Features: DICOM 3.0 compatibility, operating system, user interaction, multi-user, image import/export, acquisition device, image interferences, organization, search, storage, database, viewing, measurement, annotation, operations, security, generator control. | Matches predicate for all listed software features. Also uses previously cleared image processing software. | Same. |
  | Safety Standards Compliance: | Complies with 21 CFR 1020.30, 21 CFR 1020.31, IEC 60601-1, EN 60601-1-2, IEC 60601-1-3, IEC 60601-2-54, EN ISO 14971, EN 62366, EN 62304. | All applicable standards met. |
  | Functional Performance: All system motions, image performance. | All functions met design requirements. Image performance criteria satisfactorily met (details in Section 18 of the original submission, not provided in this extract). | Confirmed. |
  | Software/Firmware Functionality: All functions between DROC software and IntegraX firmware. | All functions passed testing criteria. | Confirmed. |

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

The document explicitly states: "It was determined that clinical evaluation was not required as all imaging devices have been previously cleared by the FDA."
Therefore, there was no clinical test set used for this specific 510(k) submission. The evaluation was based on non-clinical testing and substantial equivalence to previously cleared devices.

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

Not applicable, as no clinical test set requiring expert ground truth was used.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set requiring 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:

Not applicable. The device is a diagnostic X-ray system, not an AI-powered diagnostic tool, and no MRMC study was conducted.

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

Not applicable. The device is a diagnostic X-ray system, not an algorithm, and its performance is evaluated as a system used by a human operator.

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

Not applicable for clinical ground truth, as no clinical evaluation was performed. For non-clinical performance (image quality, functional testing, safety), the "ground truth" was compliance with design specifications and relevant international standards.

8. The sample size for the training set:

The document does not describe the use of machine learning or AI models that would require a "training set" in the traditional sense. The device is a traditional X-ray system. The component parts, such as solid-state detectors and image processing software, are stated to have been "previously cleared by the FDA" or "tested and evaluated per Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices." Therefore, any implicit training (e.g., for image processing algorithms) would have occurred as part of the development and clearance of those component devices, but details are not provided here for the system submission.

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

Not applicable, as no training set for an AI/ML model is described for this device.

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The KrystalRad 1100 and KrystalRad 3000 Digital Stationary Radiographic Systems are intended for use by a qualified/ trained doctor or technician on both adult and pediatic 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.

This device represents a new combination of already cleared solid state digital x-ray acquisition panels and software with the diagnostic x-ray compnents required to make a complete system. The purchaser may select their digital panel from this list:

• Toshiba wireless flat panel detector (FDX-3543RP, FDX-3543RPW, 14 in. x 17 in.) or Toshiba wired flat panel detector (FDX-4343R, 17 in x 17in). (K130883)
• Vieworks all series: (FXRD-1717SA/SB, or FXRD-1417SA/SB or FXRD-1417WA/WB. (K130337, Medicatech "New Series.")
• PerkinElmer XRpad™ 4336 MED, (K140551).
  The purchaser can select either a "C" arm configuration (KrystalRad 1100) or an overhead tube crane configuration (KrystalRad 3000). The x-ray generator is a CPI CMP 200DR. The x-ray tubes are supplied by Toshiba (E7252X Series), and the collimator is the Ralco R302A. An IBA kerma meter model 120-131 is supplied.

The provided text describes the KrystalRad 1100 and KrystalRad 3000 Digital Stationary Radiographic Systems. However, it does not contain detailed information regarding the acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert qualifications, or adjudication methods typically found in a clinical study report for AI-powered devices.

This document is a 510(k) premarket notification for a traditional medical device (an X-ray system), not an AI/ML-powered device that requires extensive clinical performance evaluation against specific acceptance criteria. The approval is based on substantial equivalence to a predicate device, meaning it has the same intended use and similar technological characteristics, and that its components have been previously cleared.

Here's a breakdown of the available information based on your request, highlighting what is present and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing: Specific quantitative performance metrics (e.g., sensitivity, specificity, AUC) or defined acceptance thresholds for diagnostic accuracy that would be typical for an AI device.

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

• Test Set Sample Size: Not specified. The document states "Clinical images were acquired from each panel".
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• Number of Experts: One
• Qualifications of Experts: "a board certified radiologist."

4. Adjudication Method for the Test Set

• Adjudication Method: Implicitly "none," as only one radiologist reviewed the images. There was no consensus or arbitration process mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No. This type of study is more common for assessing the impact of AI on human reader performance.
• Effect size of improvement with AI vs. without AI: Not applicable, as this is not an AI-powered device.

6. Standalone Performance Study (Algorithm Only)

• Was a standalone study done? No. This device is a radiographic system, and its performance is evaluated as an integrated system producing images for human interpretation.

7. Type of Ground Truth Used

• Ground Truth Type: Expert opinion/review ("clinical images... reviewed by a board certified radiologist").

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a traditional X-ray system, not an AI/ML model that requires a training set.

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

• How Ground Truth Was Established: Not applicable, as there is no training set for an AI/ML model.

Summary of Device and Study Context:

The KrystalRad 1100 and KrystalRad 3000 Digital Stationary Radiographic Systems are conventional X-ray machines. Their FDA clearance (K143257) is based on demonstrating substantial equivalence to an existing legally marketed device (Sedecal Nova FA DR System, K133782). The "study" described is a non-clinical bench testing and review of clinical images to ensure the system functions correctly and produces diagnostically acceptable images. It is not a clinical trial designed to establish specific performance metrics against a medical condition, nor does it involve an AI algorithm with training and test sets. The focus is on the safety and effectiveness of the hardware and integrated software for image acquisition, primarily through demonstrating that its components (cleared digital panels, generator, collimator) work together to produce images of "excellent diagnostic quality" as judged by a single radiologist.

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Radlink GPS is intended for digital image capture use in general radiographic examinations, whenever conventional screen-film systems may be used. Radlink GPS allows imaging of the pelvis, knee, skull, chest, shoulder, spine, abdomen and extremities. The digital images are transmitted from the panel or from a connection to PACS via computer networks or from a video input port to a personal computer (PC) where they may be displayed, processed, altered, overlaid with templates, compressed for archiving or transmission via computer networks to other medical facility sites. Not for mammography.

The Radlink GPS system represents the straightforward integration of digital x-ray receptor panels with their own FDA 510(k) clearance and our acquisition software that has been previously cleared by the FDA for use with our Radlink CR-Pro Solid State X-ray Imager (K052938) and Radlink LaserPro-16 (K020243). The Radlink GPS is compatible with the following digital x-ray receptor panels:

• . Vieworks VIVIX-S (K120020) and VIVIX-S Wireless (K122865)
• . Trixell Artpix Mobile EZ2GO with portable PIXIUM 3543EZ (K110849)
• -Perkin Elmer XRpad 4336 MED (K140551)
  Radlink GPS is a Digital Radiography (DR) system, featuring an integrated flat panel digital detector (FPD). Radlink GPS is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operations of the Radlink CR Pro acquisition software and PACS were not changed. The digital copies are transmitted to an internal personal computer (PC) where they may be displayed, processed for archiving or transmission via computer networks to other medical facility sites. Images can be rotated, flipped, coomed, window level, overlaid and annotated (markers, text, freestyle, line distance measurements, angles). Digital images may be received via the flat panel digital detector (FPD), from a connection to PACS via computer networks or from a video input port.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Radlink GPS device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal acceptance criteria with numerical targets for performance metrics (e.g., sensitivity, specificity, image quality scores). Instead, the "acceptance criteria" appear to be implicit in demonstrating that the device is "as safe and effective as products currently legally for sale in the USA" and "substantially equivalent to predicate devices."

The reported device performance primarily focuses on functionality, safety, and diagnostic quality rather than specific quantitative performance metrics.

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

• Test Set Sample Size: The document states that "Human images were obtained from each of the panels." It does not specify the exact number of human images or cases used for this review.
• Data Provenance: Not explicitly stated. Given the context of seeking FDA clearance in the USA, it's likely the images were acquired in a medical setting, possibly in the USA, but no specific country or retrospective/prospective nature is mentioned.

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

• Number of Experts: "a Board Certified Radiologist." (Singular)
• Qualifications of Experts: "Board Certified Radiologist." No specific number of years of experience is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: "reviewed by a Board Certified Radiologist." There is no mention of multiple readers or an adjudication process (e.g., 2+1, 3+1). The assessment appears to be a single-reader evaluation.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The document focuses on demonstrating substantial equivalence primarily through functional comparison, technical performance verification, and a limited clinical review by a single radiologist. There is no mention of comparing human readers with and without AI assistance. The device itself is an image acquisition and processing system, not an AI diagnostic aid.

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

• Standalone Performance: The Radlink GPS is a digital radiography system, not an AI algorithm in the context typically discussed for standalone performance (e.g., detecting disease). Its "performance" is inherently linked to image acquisition and display. The "algorithm" here refers to the acquisition and processing software, and its standalone performance is tested through aspects like "conformance to its defined specifications" using phantoms, software validation, and DICOM compatibility. While not referred to as "standalone AI performance," the non-clinical testing and software validation serve a similar function for the core components of the system.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the diagnostic quality assessment was based on expert consensus (from a single Board Certified Radiologist) who evaluated the human images and found them to be of "good diagnostic quality." For other aspects like image acquisition and technical specifications, the ground truth was based on physical measurements (e.g., gray-scale wedge, line resolution phantom) against predefined technical standards.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable in the context of this submission. The Radlink GPS is described as an integration of existing cleared components (digital panels and acquisition software that was previously cleared). It is not an AI/machine learning model that undergoes a distinct "training phase" on a dataset in the way a diagnostic algorithm would. The software was previously cleared (K052938, K020243), implying its development and validation occurred prior to this submission.

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

• Ground Truth for Training Set: Not applicable. As mentioned above, this is an integration of pre-cleared components, not a new AI algorithm requiring a dedicated training set and associated ground truth establishment for that training. The development and validation of the constituent software and hardware components would have involved their own respective "ground truths" at the time of their original clearance.

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