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The RFA-1717DI detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

The RFA-1717DI detector is a digital X-ray flat panel detector which has 43cm x 43cm imaging area and communicates with a wired communication feature. Giga-bit Ethernet communication method through connection of tether cable. The RFA-1717 detector is available in two types of scintillator: Csl: Tl type for RFA-1717DIC model and Gadox:Tb type for RFA-1717DIG model. The device accepts x-ray photons and the scintillator and emits visible spectrum photons that illuminate an array of photo (IGZO)-detector that creates electrical signals. After the electrical signals are generated, it is converted to digital values, and the images will be displayed on the monitor. This device should be integrated with an operating PC and an X-Ray generator. It can digitalize x-ray images and transfer them for radiography diagnostics. Advanced digital image processing allows considerably efficient diagnosis and imaging data management on network.

This document describes a 510(k) submission for the RFA-1717DI digital flat panel X-ray detector, seeking substantial equivalence to existing predicate devices. As such, the acceptance criteria and study described are for a non-clinical performance evaluation comparing the new device to established predicates, rather than a clinical study evaluating the impact of an AI algorithm on human reader performance.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating comparability to the predicate devices. The "performance" here refers to the technical specifications and measurements of the device itself, rather than diagnostic accuracy.

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

• Test Set Sample Size: The document does not specify a numerical sample size for "test sets" in the traditional sense of a clinical trial with patient cases. The evaluation is primarily based on bench testing and technical performance measurements of the device itself (e.g., MTF, DQE, NPS).
• Data Provenance: The testing was conducted by ASTEL Inc. (the manufacturer) and a 3rd party testing lab A (for electrical safety and EMC). The country of origin for the data (and manufacturing) is explicitly stated as Korea (26-79, Gajeongbuk-ro, Yuseong-gu, Daejeon, 34113, Korea). This was a prospective evaluation of the new device's performance against established standards, not a retrospective analysis of patient data.

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

• Number of Experts: Not applicable. For this type of device (a digital X-ray detector, not an AI diagnostic algorithm), "ground truth" is established via physical measurements and adherence to engineering standards, not clinical expert consensus on image interpretation.
• Qualifications of Experts: The experts would be qualified engineers and physicists specializing in medical imaging device testing and regulatory compliance. The document mentions "3rd party testing lab A," implying accredited professionals conducted the safety and performance tests.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. There is no human interpretation or diagnostic "ground truth" adjudicated for this type of device submission. The device's performance is objectively measured against physical and engineering standards.

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

• Was it done? No. This type of study (MRMC) is typically performed for AI-powered diagnostic aids, where the impact of the AI on human reader performance is being evaluated. This submission is for a basic imaging acquisition device, not an AI diagnostic tool.
• Effect Size: Not applicable.

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

• Was it done? Yes, in essence. The "non-clinical tests" and "performance testing/data" section describe the standalone performance of the RFA-1717DI detector. This includes measurements of MTF, DQE, and NPS, which are intrinsic performance metrics of the device itself, independent of human interpretation or any AI algorithm. The device's ability to produce images comparable to the predicate devices is the core of this "standalone" assessment.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for this device is based on objective physical and engineering standards and measurements. This includes:
  • International Standards: IEC 62220-1 (for DQE, MTF, NPS performance), IEC 60601-1 (electrical safety), IEC 60601-1-2 (electromagnetic compatibility).
  • Predicate Device Specifications: The performance values of the legally marketed predicate devices (LLX240AB01 and LTX240AA01) serve as the benchmark for "substantial equivalence."
  • Risk Management Standards: ISO 14971.

8. Sample Size for the Training Set

• Sample Size: Not applicable. This is a 510(k) for a hardware device (X-ray detector). It does not involve AI algorithms that require a "training set" of data.

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

• How Established: Not applicable, as there is no training set for this type of device.

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The GR40CW Digital X-ray Imaging System is intended for use in general projection radiographic applications wherever conventional screen-film systems or CR systems may be used. This device is not intended for mammographic applications.

The GR40CW digital X-ray imaging system consists of Detector, Power supply box, Battery pack, Battery charger, Access point, CIB(Control Interface Box), Workstation, Barcode scanner and Main cable. This system is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process device being sent to the S-Station (Operation Software) and saved in DICOM file, a standard for medical imaging. The captured images are sent to the Picture Archiving & Communication System (PACS) server, and can be used for reading images.

This document describes the premarket notification for the GR40CW Digital X-ray Imaging System (retrofit kit). The submission focuses on demonstrating substantial equivalence to predicate devices, particularly concerning the performance of a new detector, S4335-WV.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a pass/fail format. Instead, it relies on demonstrating equivalence or improvement compared to predicate devices for key technical specifications and imaging performance metrics.

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

The document mentions "clinical images were obtained in accordance with FDA guidance for the submission of 510(k)'s for Solid State X-Ray Imaging Devices." However, it does not specify the sample size for the test set or the data provenance (country of origin, retrospective or prospective).

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

The clinical images were "evaluated by a professional radiologist." The document mentions "a professional radiologist," implying a single expert was used. Their specific qualifications (e.g., years of experience, subspecialty) are not provided.

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

The document states the images were "evaluated by a professional radiologist." This suggests no formal adjudication method (like 2+1 or 3+1) was employed, as only one radiologist is mentioned as evaluating the images.

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

No MRMC comparative effectiveness study was done. The device GR40CW is described as a "Digital X-ray Imaging System" and a "retrofit kit" that generates digital images. It is not an AI-powered diagnostic tool, but rather a digital detector replacing analog film systems. Therefore, the question of human reader improvement with AI assistance is not applicable to this device.

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

Not Applicable. The GR40CW is a digital X-ray imaging system, not an algorithm, so a standalone performance evaluation of an "algorithm only" is not relevant. Its performance is tied to its image acquisition capabilities, which are then interpreted by human readers.

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

The ground truth for the clinical images was established by a professional radiologist's evaluation. It is not explicitly stated whether this involved objective measures like pathology or outcomes data, but rather the radiologist's interpretation of the images.

8. The sample size for the training set

The document does not specify any training set size. The context of the submission is a 510(k) for a device with a new detector, focusing on demonstrating equivalence to existing technology, not on training an AI algorithm.

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

Not Applicable. As no training set is mentioned in the context of an AI algorithm, the method for establishing ground truth for a training set is not discussed.

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The Clear Vision DR7000F product 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.

The Clear Vision DR7000F system is intended to be used in medical clinics and hospitals for emergency, orthopedic, chiropractic, and other medical purposes. This device is not indicated for use in mammography.

The Clear Vision DR7000F system is a high-resolution digital imaging system designed for digital radiography. It is designed to replace conventional film radiography techniques. This system consists of a tube head/collimator assembly mounted on a U-Arm, along with a generator, generator control, and a detector, operating software.

The detector which is used proposed device is LTX240AA01-A (K090742) and LLX240AB01 (K102587) of Samsung Mobile Display Co., Ltd. These detectors are cleared by FDA 510(k).

The provided 510(k) summary for the Clear Vision DR7000F does not contain information about acceptance criteria or a study proving the device meets specific performance criteria related to AI or algorithm-only performance.

The document describes a digital radiography X-ray system, which is a hardware device, not an AI or algorithm-based diagnostic tool. The submission focuses on demonstrating substantial equivalence to predicate hardware devices and compliance with electrical, mechanical, environmental safety, and performance standards for X-ray systems.

Therefore, most of the requested information regarding AI/algorithm performance, ground truth establishment, expert review, and training/test set sizes is not applicable to this document.

Here's a breakdown of what can be extracted or inferred from the provided text, and where information is missing / not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Regarding specific questions related to AI/Algorithm performance:

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

• Not applicable / Not provided. This device is an X-ray imaging system, not an AI algorithm. The "clinical testing" mentioned for the X-ray detectors likely refers to performance evaluation under clinical conditions, not an algorithm's diagnostic accuracy on a test set of images.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable / Not provided. No specific "ground truth" establishment for an algorithm's performance is mentioned. Evaluation of an X-ray system focuses on image quality, radiation dose, safety, and functionality, which are assessed against technical specifications and clinical utility, rather than diagnostic "ground truth" for an AI model.

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

• Not applicable / Not provided.

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 / Not provided. This submission is for a medical imaging device, not an AI-assisted diagnostic tool. No MRMC study is mentioned.

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

• Not applicable / Not provided. No standalone algorithm performance is discussed.

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

• Not applicable / Not provided. For an X-ray device, "ground truth" generally relates to physical measurements (e.g., spatial resolution, contrast-to-noise ratio, MTF, DQE) and clinical image quality (diagnostic acceptability) rather than a pathology reference for an AI diagnosis.

8. The sample size for the training set

• Not applicable / Not provided. No AI training set is mentioned.

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

• Not applicable / Not provided. No AI training is mentioned.

Summary regarding the device:

The Clear Vision DR7000F is a digital radiography X-ray system. The study proving it meets acceptance criteria primarily involves engineering and performance testing against established international standards (EN/IEC 60601 series) for medical electrical equipment, as well as specific guidance for solid-state X-ray imaging devices. The acceptance criteria relate to electrical, mechanical, environmental safety, electromagnetic compatibility (EMC), and the technical performance and clinical utility of the X-ray detectors. The "study" mentioned is the compilation of these satisfactory test results conducted by the manufacturer, demonstrating compliance and substantial equivalence to existing cleared predicate hardware devices.

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

The ViZion DR system represents the straightforward integration of two cleared devices: ViZion DR, K102123 and K102587, the Samsung Digital Flat Panel. ViZion DR is a Digital Radiography system, featuring an integrated flat panel digital detector (FPD) (K102587, Samsung Flat-Panel X-Ray Detector), made by Samsung Mobile Display Co., Ltd. (this is the 510(k) for the flat panel detector) and Viztek's proprietary OPAL-RAD PACS image viewing and acquire interface software technology, (K063337) which incorporates state of the art object-oriented software and connectivity. ViZion 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 film less. environment and improves cost effectiveness. The major functions and principle of operation of the Viztek PACS and Samsung FPD were not changed.

This is a 510(k) premarket notification for a new version of the Viztek ViZion DR, a Digital Radiography (DR) system. The submission focuses on replacing the digital flat panel detector (FPD) with a new model while maintaining the existing software and overall functionality. As such, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are framed in terms of demonstrating substantial equivalence to a predicate device, rather than proving a specific diagnostic accuracy against a clinical ground truth.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the demonstration of substantial equivalence to the predicate device, specifically regarding safety and effectiveness, and the absence of new indications for use or technological differences that would raise new questions of safety or effectiveness. The reported performance is a comparison to the predicate.

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

The document states: "Clinical images collected demonstrate equal or better image quality as compared to our predicates." However, it does not specify the sample size for this clinical image inspection, nor does it provide details about the data provenance (e.g., country of origin, retrospective or prospective nature). Given the nature of a 510(k) for a component change (the FPD), this might have been a limited comparative study rather than a large-scale clinical trial.

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

The document mentions "clinical image inspection" but does not provide any information on the number of experts involved in this inspection or their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method. It only mentions "clinical image inspection."

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, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done. This device is a digital X-ray detector system, not an AI-assisted diagnostic tool. The comparison is between the new detector and a previous detector system, with the focus on image quality and equivalence, not reader performance improvement with AI.

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

This refers to an algorithm's performance without human intervention. Since the device is a digital X-ray detector system (hardware), not a diagnostic algorithm, this concept does not apply. The "standalone performance" for this device would relate to its hardware specifications and image acquisition capabilities, which are covered by bench and laboratory testing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The available information suggests that the "ground truth" for the comparison was based on "clinical image inspection" and possibly "bench, and test laboratory" results, comparing the image quality of the new device to that of the predicate device. This implies a subjective assessment of image quality by potentially experts, but the specifics are not detailed. It is unlikely to involve pathology or outcomes data for this type of 510(k) submission.

8. The Sample Size for the Training Set

This submission is for a digital X-ray detector system, not an AI algorithm that requires a training set. Therefore, there is no training set in the context of this device.

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

As there is no training set for this hardware device, this question is not applicable.

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