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Mercu1717V Digital Flat Panel Detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Mercu1717V Digital Flat Panel Detectors (Hereinafter referred to as Mercu1717V) supports dynamic imaging and static imaging. The sensor plate of Mercu1717V is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the cable. The major function of the Mercu1717V is to convert the X-ray to digital image, with the application of high-resolution X-ray imaging. Mercu1717V can get single image and it also can get dynamic image. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. The iRay DR used for digital X-ray radiography image from the digital flat panel detectors. iRay DR is used to handle the DICOM protocol (DICOM 3.0). iRay DR has many functions such as image acquisition, image enhancement processing and editing image or information.

The provided document does not contain detailed information about specific acceptance criteria and a study proving the device meets them in the context of clinical performance or human reader studies. Instead, it focuses on the device's technical specifications and substantial equivalence to predicate devices based on non-clinical testing.

Here's an analysis based on the available information, noting where specific details are missing:

1. Table of Acceptance Criteria and Reported Device Performance (Based on provided technical specifications for comparison with predicate devices):

The document primarily compares various technical specifications of the Mercu1717V with its predicate and reference devices, aiming to demonstrate substantial equivalence rather than explicit acceptance criteria and performance against those criteria as would be typical for clinical effectiveness. However, we can extract some performance metrics from the comparison table.

Missing Information:
The document does not explicitly state acceptance criteria in terms of clinical performance or diagnostic accuracy. Instead, it demonstrates an equivalence to predicate devices through technical specifications and compliance with safety standards. The "study that proves the device meets the acceptance criteria" refers to non-clinical testing performed to show substantial equivalence.

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

• Sample Size: Not applicable in the context of clinical images or patient data tests for this submission. The "test set" here refers to the actual device undergoing non-clinical technical evaluations (e.g., electrical safety, EMC, image quality parameters like MTF, DQE).
• Data Provenance: Not applicable. The evaluations are technical measurements of the physical device under specific lab conditions.

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

• Not applicable. This device is a digital flat panel detector, a hardware component for acquiring X-ray images. The submission focuses on its physical characteristics, safety, and image quality parameters, not on the interpretation of images by experts. Ground truth in this context would relate to the objective measurement of physical properties.

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

• Not applicable. No expert adjudication of diagnostic outcomes is mentioned or implied, as this is a device component clearance and not a diagnostic AI software submission.

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 as this submission pertains to a digital X-ray detector, not an AI-powered diagnostic tool requiring human-in-the-loop performance evaluation.

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

• No standalone algorithm performance study was done for diagnostic purposes. The device is a hardware component. Its "standalone" performance relates to its physical performance metrics (e.g., DQE, MTF) as measured in a lab setting.

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

• For the non-clinical studies (e.g., MTF, DQE, electrical safety), the "ground truth" would be established by standardized measurement protocols and reference instruments, as per relevant IEC or other industry standards. It's objective, physical measurements rather than clinical ground truth from patient data.

8. The sample size for the training set:

• Not applicable. This document is for a hardware device (digital flat panel detector) and its associated software (iRayDR), which is described as image acquisition, processing, and archiving software, not a machine learning model that requires a training set of medical images.

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

• Not applicable, as there is no mention of a machine learning model or a training set. The software mentioned (iRayDR) performs image acquisition and post-processing, typical for a radiological workstation, not AI-driven diagnosis.

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Indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures, excluding fluoroscopic, angiographic, and mammographic applications.

The Prudent 1717, Prudent 1417, Prudent 1212 are digital radiography systems, featuring an integrated flat panel digital detector (FPD). It 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 operation of the updated panels are the same as our previous panel retaining the Wi-Fi wireless features and rechargeable battery operation. The Prudent 1717 is available in 3 pixel sizes: 100/140/168 um whereas the Prudent 1417, Prudent 1212 are available in two pixel sizes: 100/140 µm. The available resolutions vary according to the comparison table below. All of the models are Wi-Fi wired) and rechargeable battery (or AC line) operated. The device employs the same software as cleared in the predicate with only minor changes made.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the PIXXGEN Corporation's Prudent digital x-ray detector panels:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the comparison to predicate devices and adherence to established standards. The reported device performance is presented in comparison to these predicates.

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

• Sample Size for Test Set: The document does not specify a numerical sample size for the "clinical images" test set. It only states "Clinical images collected."
• Data Provenance: The document does not explicitly state the country of origin. It does not explicitly state if the data was retrospective or prospective. However, the term "Clinical images collected" typically implies prospective collection for such validation, but this is not explicitly confirmed.

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

• Number of Experts: "a Board Certified Radiologist" (singular, implying one).
• Qualifications of Experts: "Board Certified Radiologist." No specific experience level (e.g., "10 years of experience") is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: The document states that the images were "evaluated by a Board Certified Radiologist." This suggests a single reader evaluation, which means no multi-reader adjudication method (like 2+1, 3+1) was explicitly performed or mentioned for the clinical image evaluation.

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 mentioned. The study described is an evaluation of the device's image quality by a single radiologist, not a comparison of human readers' performance with and without AI assistance. This device is a digital x-ray detector panel, not an AI-powered image analysis tool.

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

• The document primarily describes a standalone performance evaluation of the imaging device itself (the detector panel) in terms of objective image quality metrics (DQE, MTF, limiting resolution) and a qualitative assessment of clinical images. Since the device is a detector, it intrinsically operates "standalone" in providing the image data. The "algorithm" here refers to the device's inherent image acquisition and processing capabilities, not an AI algorithm acting on those images. The evaluation by the radiologist is an assessment of the output of the standalone device.

7. The Type of Ground Truth Used

• For Quantitative Metrics (DQE, MTF, Limiting Resolution): These are objective physical measurements governed by established scientific and engineering standards (e.g., Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices). The "ground truth" for these is the measurement itself, verified against the alternate predicate devices' published specifications.
• For Clinical Images: The ground truth was established by expert consensus/evaluation by a "Board Certified Radiologist." The assessment was subjective, stating the images were "of excellent diagnostic quality." It is not directly pathology or outcomes data.

8. The Sample Size for the Training Set

• This document describes a medical device (digital x-ray detector panel), not an AI algorithm that requires a separate "training set" in the machine learning sense. Therefore, there is no mention of a training set sample size. The device's "training" refers to its design and engineering to meet specific technical specifications.

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

• As a digital x-ray detector panel, the concept of a "training set" and establishing ground truth for it (in the AI/machine learning context) does not apply. The device's performance is driven by its physical components and embedded firmware/software, which are developed and verified through engineering principles and adherence to standards rather than algorithm training on a dataset.

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