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INNOVISION-EXII is a stationery X-ray system intended for obtaining radiographic images of various anatomical parts of the human body, both pediatrics and adults, in a clinical environment. INNOVISION-EXII is not intended for mammography, angiography, interventional, or fluoroscopy use.

INNOVISION-EXII can receive X-ray signals from X-ray irradiation and digitize them into X-ray images by converting digital images to DICOM image format using Elui imaging software. INNOVISION-EXII is a general radiography X-ray system and not for mammography nor fluoroscopy. In addition, the system must be operated by a user who is trained and licensed to handle a general radiography X-ray system to meet the regulatory requirements of a Radiologic Technologist. Target areas for examinations include the head, spine, chest, and abdomen for diagnostic screening of orthopedic, respiratory, or vertebral discs. The system can capture a patient's postures, such as sitting, standing, or lying. This system can be used for patients of all ages, but it should be used with care for pregnant women and infants. The INNOVISION-EXII system has no part directly touching the patient's body.

The provided text describes a 510(k) summary for the INNOVISION-EXII stationary X-ray system, asserting its substantial equivalence to a predicate device (GXR-Series Diagnostic X-Ray System). However, the document does not contain information about acceptance criteria or a detailed study proving the device meets specific acceptance criteria related to its performance metrics for diagnostic imaging or AI assistance.

The "Clinical testing" section on page 9 merely states: "Clinical image evaluation of INNOVISION-EXII has been performed. The evaluation results demonstrated that INNOVISION-EXII generated images are adequate and suitable for expressing contour and outlines. The image quality including contrast and density are appropriate and acceptable for diagnostic exams." This is a very general statement and does not provide specific acceptance criteria or detailed study results.

Similarly, there are no details regarding AI performance (standalone or human-in-the-loop), sample sizes, ground truth establishment, or expert qualifications for such studies. The document focuses on establishing substantial equivalence based on intended use, technological characteristics, and compliance with various safety and performance standards (electrical safety, EMC, software validation, risk analysis).

Therefore, based solely on the provided text, the requested information about acceptance criteria and a study proving the device meets these criteria cannot be extracted or inferred. The document is a 510(k) summary focused on demonstrating substantial equivalence, not a detailed clinical performance study report.

Here is a breakdown of why each requested point cannot be addressed from the given text:

1. A table of acceptance criteria and the reported device performance: Not present. The "clinical testing" section is too vague.
2. Sample sized used for the test set and the data provenance: Not present. No specific test set for clinical performance is detailed.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not present. No ground truth establishment process is described beyond a general "clinical image evaluation."
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not present.
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 present. The document does not mention any AI component or MRMC study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not present. No mention of an algorithm or standalone performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not detailed. Only a general "clinical image evaluation" is mentioned.
8. The sample size for the training set: Not present. The document describes a medical imaging device, not a machine learning model requiring a training set.
9. How the ground truth for the training set was established: Not applicable, as there's no mention of a training set or machine learning components.

In summary, the provided FDA 510(k) summary largely focuses on engineering and regulatory compliance (electrical safety, EMC, software validation, comparison of technical specifications to a predicate device) to establish substantial equivalence, rather than detailed clinical performance metrics derived from a study with specific acceptance criteria and ground truth for diagnostic accuracy.

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

This device represents a combination of an already cleared solid state digital x-ray acquisition panel with software and diagnostic x-ray components required to make a complete system. Film cassettes may be employed in place of the x-ray generator has been changed to a Delta Electronics Delta DMP 100R The collimator has been changed to a Fairy Medical Electronics model CRUX FR04. The tubehead has been changed to a Hangzhou Kailong Medical Instruments Co., Ltd H1074X. The system complies with the CDRH Radiological Health performance standard in the Code of Federal Regulations, as well as the voluntary IEC standards IEC 60601-1 and IEC 60601-1-2.

The provided text is a 510(k) Pre-market Notification document for the "Jumong General" stationary x-ray system. This document is a submission to the FDA demonstrating that a new device is substantially equivalent to a legally marketed predicate device.

It's important to understand that in this context, "acceptance criteria" and "study that proves the device meets the acceptance criteria" refer to the demonstration of substantial equivalence, primarily through non-clinical performance testing and comparison to an established predicate device, rather than a clinical study with human-in-the-loop performance measurement of an AI algorithm.

The document states:

• "5. Safety and Effectiveness, comparison to predicate device. This combination device has the same indications for use and very similar technological characteristics as the predicate device, and employs already 510(k) cleared digital panels and software."
• "No software modifications were made to the imaging chain."
• "8. Summary of clinical testing: Not applicable."

This clearly indicates that this submission does not involve an AI algorithm that requires a separate performance study with human readers or standalone algorithm performance assessment. The device is a conventional x-ray imaging system, albeit with some updated components compared to its predicate.

Therefore, many of the requested points related to AI performance, such as MRMC studies, standalone AI performance, ground truth establishment for AI training/testing, and expert adjudication, are not applicable to this specific submission.

However, I can still interpret how the concept of "acceptance criteria" and "proof" apply to this device based on the provided text, focusing on its non-clinical performance and substantial equivalence demonstration.

Acceptance Criteria and Study for Jumong General X-Ray System

Given that the Jumong General is a stationary x-ray system (Product Code: KPR, MQB) and not an AI-powered diagnostic device, the "acceptance criteria" and "proof" focus on demonstrating substantial equivalence to a previously cleared predicate device (K150816, Jumong Series Stationary Radiographic System). This is achieved primarily through non-clinical performance testing, safety standards compliance, and direct comparison of functional characteristics. The document explicitly states "No software modifications were made to the imaging chain," and "Clinical testing: Not applicable."

The "acceptance criteria" for demonstrating substantial equivalence primarily revolve around meeting recognized performance standards, electrical safety, EMC compliance, and demonstrating comparable image quality and intended use to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The new components are:

• Generator: Delta Electronics DMP 100R (same kVp range as CPI CMP 200 DR predicate)
• Collimator: Fairy Medical Electronics CRUX FR04 (changed from Ralco Model R225)
• Tubehead: Hangzhou Kailong Medical Instruments Co., Ltd H1074X (changed from Varian RAD14)

Digital Panel Models, Image acquisition panel specifications, DICOM, Image acquisition software, and Power Source are all SAME as predicate. (page 5, comparison chart).

Conclusion: "This combination device has the same indications for use and very similar technological characteristics as the predicate device, and employs already 510(k) cleared digital panels and software." (page 4, section 5) |
| Safety & Performance Standards | Compliance with relevant US Radiation Safety Performance Standards and IEC standards for medical electrical equipment. | - Conforms to the US Performance Standard. (page 6, section 7)

• New generator complies with: IEC 60601-2-54:2009+A1+A2, IEC 60601-1:2005+Corrigendum 1+Corrigendum 2+A1, IEC 60601-1-3:2008+A1, and IEC 60601-1-6:2010+A1. (page 6, section 7)
• EMC testing complies with IEC 60601-1-2:2014. (page 6, section 7)
• New collimator tested for compliance with EN/IEC 60601-2-54 for radiation leakage. (page 6, section 7)
• New tubehead tested to IEC 60613:2010 and IEC 60336:2005. (page 6, section 7)
• "Every unit is tested for electrical safety, input power, display of operation and exposure factors, collimator operation, reproducibility, and accuracy." (page 6, section 7) |
  | Image Quality | Image quality should be diagnostically acceptable and comparable to the predicate device. | "Test images were acquired which showed excellent diagnostic quality." (page 6, section 7) |
  | Risk Analysis | Assessment of risks associated with modifications and demonstration of acceptable risk profile. | "A risk analysis was performed with regard to the modifications. No software modifications were made to the imaging chain." (page 6-7, section 7) The conclusion states the device is "as safe and effective as the predicate device." (page 7, section 9) |

As this is a conventional X-ray system submission and not an AI/CADe device, the following points are largely "Not Applicable."

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

• Not Applicable in the traditional sense of a clinical test set for an AI algorithm. The "test set" here refers to the physical system undergoing non-clinical verification and validation tests. The document references "test images" acquired for image quality assessment, but the sample size or specific provenance of these images is not detailed as it would be for a clinical AI study. The testing is described as non-clinical ("integration and image quality testing").

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

• Not Applicable. Ground truth, in the context of diagnostic accuracy for AI, is not relevant here. The "diagnostic quality" of images was assessed, presumably by qualified personnel, but this is a standard engineering and quality assurance assessment for image generators, not an AI ground truth establishment process.

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

• Not Applicable. This pertains to clinical AI studies for diagnostic accuracy.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not Applicable. This is specific to AI-assisted diagnostic devices. The document explicitly states "Summary of clinical testing: Not applicable."

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

• Not Applicable. This is specific to the performance of an AI algorithm. The device is a hardware x-ray system, and no AI algorithm's standalone performance is being evaluated.

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

• Not Applicable. Ground truth for diagnostic accuracy is not relevant here as it's a hardware device demonstrating fundamental imaging capability and safety, not a diagnostic interpretation tool.

8. The sample size for the training set

• Not Applicable. There is no AI algorithm being trained for this device as per the submission details ("No software modifications were made to the imaging chain").

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

• Not Applicable. As there is no AI algorithm being trained by the applicant, this point is not relevant.

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VIVIX-S 1012N (FXRD-1012NA, FXRD-1012NB, FXRD-1012NAW and FXRD-1012NBW) is indicated for digital imaging solution designed as a general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purposes of diagnostic procedures. It is not to be used for mammography.

Models FXRD-1012NA, FXRD-1012NB, FXRD-1012NAW and FXRD-1012NBW intercept X-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photo (a-SI)-detectors that create an electrical signals. After the electrical signals are generated, these are converted to a digital value, and an image will be displayed on the monitor.

These devices should be integrated with an operating PC and an X-Ray generator to digitalize Xray images and transfer the digitalized images for radiography diagnostic.

Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and image information sharing on the network.

The provided text does not contain detailed acceptance criteria or a comprehensive study design that allows for the extraction of all requested information. The document is a 510(k) summary for a digital X-ray detector, focusing on demonstrating substantial equivalence to predicate devices rather than a detailed performance study against specific acceptance metrics derived independently.

However, based on the available information, here's a breakdown of what can be inferred and what is missing:

Missing Information:

• Detailed, explicit acceptance criteria values (e.g., specific thresholds for DQE, MTF that had to be met by the new device, beyond "similar"). The document states "...meet the acceptance criteria...", but these criteria themselves are not listed with numerical targets.
• The exact sample size for the clinical test set (only "a single-blinded concurrence study" is mentioned).
• Number and qualifications of experts for ground truth establishment.
• Adjudication method for the test set.
• Details of any Multi-Reader Multi-Case (MRMC) comparative effectiveness study, including effect size.
• Standalone algorithm performance (this device is a digital X-ray detector, not an AI algorithm, so this concept doesn't directly apply).
• Sample size for the training set (as this is a medical device, not an AI model, there isn't a "training set" in the typical sense of machine learning).
• How ground truth for the training set was established (again, not applicable to this type of device).

Inferred Information based on the document:

The study primarily focuses on demonstrating substantial equivalence to predicate devices (K122865 and K122866) by comparing technological characteristics and performance metrics.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" with numerical thresholds that the new device must meet. Instead, it compares the performance of the subject device (VIVIX-S 1012N models) to the predicate devices. The "acceptance" is implicitly that the performance of the new device is similar or better than the predicate devices, particularly in areas like DQE and MTF.

Here's a table showing the performance comparison provided, which serves as the "reported device performance" against the implicitly accepted "similar to predicate" standard:

Note: For DQE and MTF values, the table for the "Reference Predicate Devices" (K122865) shows "-" for these performance metrics, while the "Primary Predicate Devices" (K122866) has values. The "Comparison test" in section 9 explicitly states comparison was done with K122866.

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

• Sample Size: Not explicitly stated for the clinical test set, only mentioned as "a single-blinded concurrence study."
• Data Provenance: Not specified (e.g., country of origin). The study is described as a "clinical test" which "complied with the requirements specified in the CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices," suggesting a prospective or collected dataset for the purpose of the submission. It's not explicitly stated as retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The study is described as a "single-blinded concurrence study." This implies that evaluators were blinded to which images came from the new device versus the predicate, and their interpretations were compared for "concurrence," but the method of resolving discrepancies or establishing definitive ground truth from multiple readers is not detailed.

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

• Was it done?: The document describes a "single-blinded concurrence study" comparing images from the new device to the predicate device to confirm "equivalent diagnostic capability." This is a form of comparative study involving human readers (though the number of readers is not disclosed), but it is focused on demonstrating equivalence (non-inferiority) rather than improvement with AI assistance.
• Effect Size of Human Reader Improvement: Not applicable, as this is a comparison between two different X-ray detectors, not an AI-assisted workflow vs. unassisted human reading.

6. Standalone (Algorithm Only) Performance

• Was it done?: Not applicable. The device (VIVIX-S 1012N) is a digital X-ray detector, a hardware component that captures images. It is not an AI algorithm. The performance metrics (DQE, MTF, Resolution) are intrinsic to the image capture capabilities of the detector.

7. Type of Ground Truth Used

• Type of Ground Truth: For the "concurrence study," the ground truth was based on the "equivalent diagnostic capability" of the images as assessed by human readers ("experts"). This would typically mean that clinical interpretations of images from the new device were compared to interpretations of images from the predicate device to ensure they did not differ significantly in diagnostic content. It is a form of expert consensus on image diagnostic quality, rather than a definitive "true positive/negative" based on pathology or outcomes data.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a digital X-ray detector, not an AI model requiring a training set.

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

• How Ground Truth Established: Not applicable.

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VIVIX-S 1717N (FXRD-1717NA, FXRD-1717NB, FXRD-1717NAW and FXRD-1717NBW) is indicated for digital imaging solution designed as a general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purposes of diagnostic procedures. It is not to be used for mammography.

Models FXRD-1717NA, FXRD-1717NB, FXRD-1717NAW and FXRD-1717NBW intercept X-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photo (a-SI)-detectors that create an electrical signals. After the electrical signals are generated, these are converted to a digital value, and an image will be displayed on the monitor.

These devices should be integrated with an operating PC and an X-Ray generator to digitalize Xray images and transfer the digitalized images for radiography diagnostic.

Advanced digital image processing allows considerably efficient diagnosis, all kinds of information management, and image information sharing on the network.

Models XRD-1717NA, FXRD-1717NB, FXRD-1717NAW and FXRD-1717NBW are digital X-ray flat panel detectors, and each model has a 10 x 12 inch imaging area.

FXRD-1717NA and FXRD-1717NB communicate by using a wired communication feature (Gigabit Ethernet communication method by connecting to a tether cable), while FXRD-1717NAW and FXRD-1717NBW communicate by using a wireless communication feature (IEEE 602.lla/b/g/n).

The scintillator used in FXRD-1717NA and FXRD-1717NAW is Csl. Gadox was used for FXRD-1717NB and FXRD-1717NBW.

The FXRD-1717N series is designed to be used with any certified X-ray generators that features DR Trigger mode and is marketed legally. When the DR Trigger mode is not desired, then the connection with the generator can be maintained with AED mode. FXRD-1717N is not designed to function as an X-ray control. The AED mode does not require integration procedure since there is no connection requirement between the X-ray System and the detector. The subject device can receive any types of x-ray signals without SW.

For the DR Trigger mode, the generator interface cable connects the SCU and the X-ray generator. The head of the cable is connected with one of the port (EXT-INF) of the SCU, and the other end of the cable (which is stripped) is connected to the generator's socket.

This document (K152894) describes a 510(k) premarket notification for the VIVIX-S 1717N digital flat panel X-ray detector. It focuses on demonstrating substantial equivalence to predicate devices rather than proving a specific clinical acceptance criterion with a novel AI device. Therefore, much of the requested information about AI performance, multi-reader multi-case studies, and detailed ground truth for training data is not applicable or available in this document.

However, I can extract and infer information about the acceptance criteria for a medical imaging device's performance through comparison to a predicate device, as well as the study conducted to demonstrate this equivalence.

Here's the breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" for quantitative clinical performance metrics of the device in a table format as one might expect for a new AI diagnostic device. Instead, it demonstrates substantial equivalence to predicate devices based on technological characteristics and image quality assessments. The "acceptance criteria" are implicitly met if the device's performance metrics are comparable to or better than the predicate's.

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

• Test Set Sample Size: Not explicitly stated in terms of number of images or patients. The document only mentions "a single-blinded concurrence study."
• Data Provenance: Not specified in the document (e.g., country of origin). The study is described as a "clinical test, which complied with the requirements specified in the CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices." This suggests a prospective or retrospective collection of images for the comparison, but details are not provided. The study confirms "equivalent diagnostic capability" of the new detectors to the predicate device.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The study is referred to as a "single-blinded concurrence study," which implies some form of assessment by experts, but the exact method of combining opinions or establishing ground truth isn't detailed.

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

• Was an MRMC study done? The document describes a "single-blinded concurrence study" to assess "equivalent diagnostic capability." While this involves readers and cases, the text does not indicate it was designed as a comparative effectiveness study comparing human readers with AI vs. without AI assistance. The device itself is an X-ray detector, not an AI interpretation algorithm.
• Effect Size of Human Readers with AI vs. without AI assistance: Not applicable, as this is a device for image acquisition, not AI for image interpretation.

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

• Was a standalone study done? Not applicable in the context of an AI algorithm. This device is an X-ray detector. Its performance was evaluated based on physical detector characteristics (DQE, MTF, spatial resolution) and a "concurrence study" of its images against a predicate, implying human interpretation of the images produced by the detector.

7. Type of Ground Truth Used for the Test Set

• Type of Ground Truth: The "single-blinded concurrence study" evaluated "equivalent diagnostic capability." This implies that the ground truth for image quality was established through expert assessment of the diagnostic content of the images produced by the subject device compared to the predicate device. It is a form of expert consensus on image diagnosticity, rather than pathology or long-term outcomes data primarily.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This document describes a medical imaging device (X-ray detector), not an AI algorithm that requires a training set. The performance data relates to the physical characteristics of the detector and the quality of the images it produces.

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

• How Ground Truth for Training Set Was Established: Not applicable, as this is a medical imaging device, not an AI algorithm.

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

This device represents a new combination of an already cleared solid state digital x-ray acquisition panel with software and diagnostic x-ray compnents required to make a complete system. Film cassettes may be employed in place of the digital panel. The purchaser can select from one of four configurations. The x-ray generator is a CPI CMP 200DR. The x-ray tubes are supplied by Varian (RAD-14), and the collimator is the Ralco R225 ACS DHHS. The system complies with the CDRH Radiological Health performance standard in the Code of Federal Regulations, as well as the voluntary IEC standards IEC 60601-1 and IEC 60601-1-2. All major components are either UL or CSA listed.

The provided text is a 510(k) summary for the "Jumong Series Stationary Radiographic System." It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing specific acceptance criteria and performance data from a standalone study. Therefore, much of the requested information cannot be extracted directly from this document.

Here's what can be gathered:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state quantitative acceptance criteria for image quality or clinical performance that the device was tested against. Instead, it relies on demonstrating that the device meets general safety and performance standards, and that its components (like digital panels) are previously cleared. The "performance" assessment is framed as "bench, safety test, and software validation documentation indicates that the new device is as safe and effective as the predicate device" and that the device "conforms to US Performance Standards" and various IEC/NEMA voluntary standards.

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

The document mentions that "Clinical images were provided," but does not specify the sample size, data provenance (e.g., country of origin), or whether they were retrospective or prospective. It explicitly states these images "were not necessary to establish substantial equivalence."

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

This information is not provided in the document. The phrase "clinical images were provided" suggests some form of evaluation but no details on expert involvement or ground truth establishment for a test set.

4. Adjudication Method:

This information is not provided in the document.

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

No MRMC comparative effectiveness study is mentioned in the document. The submission focuses on substantial equivalence based on technical characteristics and safety/performance standards, rather than a clinical trial comparing human reader performance.

6. Standalone Performance Study:

The document implies that images were used to show the "complete system works as intended," suggesting some form of standalone evaluation. However, it does not describe a formal standalone performance study with specific metrics, acceptance criteria, or a detailed methodology. The "non-clinical testing" section primarily covers integration, bench, safety, and software validation.

7. Type of Ground Truth Used:

The document does not explicitly state the type of ground truth used for any clinical image evaluation, given that the clinical images were "not necessary to establish substantial equivalence."

8. Sample Size for the Training Set:

This information is not applicable as the document describes a device (X-ray system), not an AI algorithm that would typically require a training set.

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

This information is not applicable as the document describes a device (X-ray system), not an AI algorithm.

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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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Agfa's DX-D Imaging Package is indicated for use in general projection radiographic applications to capture for display diagnostic quality radiographic images of human anatomy. The DX-D Imaging Package may be used wherever conventional screen-film systems may be be used.

Agfa's DX-D Imaging Package is not indicated for use in mammography.

Agfa's DX-D Imaging Package is a solid state flat panel x-ray system, a direct radiography (DR) system (product code MQB) intended to capture images of the human body. It is a combination of Agfa's NX workstation and one or more flat-panel detectors.

This submission is to add the DX-D40C/G Flat Panel Detector to Agfa's DX-D Imaging Package portfolio. Agfa's DX-D40C/G is currently marketed by Vieworks as the ViVIX-S Wireless Panel (K122865), which is one of predicates for this submission.

Principles of operation and technological characteristics of the new and predicate devices are the same. The new device is physically and electronically identical to both predicates, K121095 and K122865. It uses the same workstation as predicate K121095 and the same scintillatorphotodetector flat panel detectors to capture and digitize the images as predicate K122865.

I am sorry but I can't fulfill your request. The document describes that the new device, Agfa's DX-D Imaging Package, is substantially equivalent to two predicate devices (K121095 and K122865) and does not provide explicit acceptance criteria with specific numerical thresholds for performance metrics. This makes it difficult to directly populate the "Acceptance Criteria" column of the table you requested with quantitative values. Also, the document states "image quality clinical evaluations" were done but lacks the details of such a study. Without additional information, I am unable to describe the acceptance criteria and study as requested.

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Intended for use by a qualified/trained doctor or technologist. As part of a radiographic system, the KrystalRad "New Series" is intended to acquire digital radiographic images. It is suitable for all routine radiography exams, including specialist areas like intensive care or trauma work, excluding fluoroscopy, angiography and mammography.

This device represents the combination of already cleared software and already cleared digital receptor panels. This device is a functional replacement for radiographic film. It serves as an upgrade to film based or older digital panel diagnostic x-ray systems. Digital radiography uses digital X-ray sensors instead of traditional photographic film. Advantages include time efficiency through bypassing chemical processing and the ability to digitally transfer and enhance images. Also less radiation can be used to produce an image of similar contrast to conventional radiography. This gives advantages of immediate image preview and availability; elimination of costly film processing steps; a wider dynamic range, which makes it more forgiving for over- and under-exposure; as well as the ability to apply special image processing techniques that enhance overall display of the image.

This document describes the premarket notification (510(k)) for the KrystalRad "New Series" Digital Radiographic Portable Retrofit System. The device is intended as an upgrade for radiographic film-based or older digital panel diagnostic x-ray systems.

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not explicitly define "acceptance criteria" in a quantitative manner for performance metrics like sensitivity, specificity, or image quality scores. Instead, the demonstration of safety and effectiveness relies on establishing substantial equivalence to a predicate device (KrystalRad 660, K112132). The "acceptance criteria" in this context are primarily the absence of significant differences in technical characteristics and diagnostic quality, and compliance with relevant safety and performance standards.

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

• Sample Size for Test Set: The document states that "Clinical images were acquired and compared to our predicate images by a board certified radiologist." However, it does not specify the number of clinical images or cases used in this comparison.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the context of a 510(k) submission to the FDA, it is highly likely that the images would have been collected in a manner compliant with US regulations, potentially from a US healthcare setting. The term "acquired" suggests prospective collection for the purpose of the comparison, but this is not definitively stated.

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

• Number of Experts: One (1) board certified radiologist.
• Qualifications of Experts: Board certified radiologist. No further details on years of experience or specific sub-specialty are provided.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable. Only one radiologist was used for comparison, so there was no need for an adjudication method. The radiologist directly compared the images from the new device to those from the predicate device.

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

• MRMC Comparative Effectiveness Study: No, an MRMC comparative effectiveness study was not performed, nor was it applicable. This device is a digital X-ray panel and software system, not an AI-assisted diagnostic tool. Its purpose is to acquire and display images, not to interpret them or provide AI assistance to readers. Therefore, there is no AI component for human readers to improve with, and no effect size on human reader performance is reported.

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

• Standalone Performance Study: No, a standalone performance study in the context of an "algorithm only" or AI performance was not done, nor was it applicable. The device is a hardware and software system for image acquisition and display, not an AI algorithm. Its performance is assessed through its ability to produce diagnostically acceptable images, not through an independent algorithm's diagnostic accuracy.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The "ground truth" for the clinical image comparison was established by the expert opinion (board certified radiologist), who assessed the "diagnostic quality" and "significant differences" of the images from the new device compared to the predicate device. This is a form of expert consensus, although with only one expert, it's essentially a single expert's opinion. There is no mention of pathology, outcomes data, or other objective measures for ground truth.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: The document does not mention a training set in the context of machine learning or AI development. This device is a digital X-ray system, and its development process, as described, involves engineering and integration of already cleared components. No AI model training is indicated.

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set or AI model development.

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