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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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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 applications

The PIXX2430 is s digital radiography system, 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 panels (K182533, PIXX 1717, PIXX 1212) retaining the Wi-Fi wireless features and rechargeable battery operation. The scintillator is Csl only. The only size available is 12 x 10 inch. It operates either wirelessly or by hard wired Ethernet connection. The power source is rechargeable battery, which lasts for 360 images or 6 hours in standby. It has a finer than usual pixel pitch at 85 µm (finer resolution). Our imaging software is unchanged from our predicate device, K182533. Image storage functionality: PIXX2430 supports the internal storage of raw image data. Wireless Information: This digital panel employs the same wireless functionality as our predicate panels (K182533) using IEEE802.11ac, backward compatible. The operational characteristics can be summarized this way: Transfer power, ~ 100mW; Frequency: 2.4 gHz, or 5 gHz. Security, WPA2; Signal range: Approximately 100 feet. Both medical and nonmedical devices can use IEEE802.11ac Wi-Fi, and this technology is designed to handle multiple devices using the same technology simultaneously.

The provided text is a 510(k) summary for the PIXX2430 Digital Diagnostic X-Ray Receptor Panel. It focuses on demonstrating substantial equivalence to a predicate device (K182533) rather than defining and proving acceptance criteria for an AI/ML powered device. Therefore, much of the requested information regarding AI/ML acceptance criteria, study design (MRMC, standalone), ground truth adjudication, and training/test set details is not present in the provided document.

However, based on the non-AI device context, I can extrapolate and provide information where available, and indicate where the information is missing.

Here's an attempt to answer your request based on the provided document, highlighting the missing AI/ML specific details:

The PIXX2430 Digital Diagnostic X-Ray Receptor Panel is a digital radiography system intended to replace radiographic film/screen systems for general radiographic images of human anatomy. The acceptance criteria and the study proving the device meets these criteria are framed within the context of demonstrating substantial equivalence to a predicate device (K182533), rather than the performance of an AI/ML algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

Given this is a non-AI device seeking substantial equivalence, the "acceptance criteria" are generally aligned with demonstrating that the new device is "as safe and effective" as the predicate. The performance metrics focus on image quality and physical/electrical characteristics.

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

• Sample Size for Test Set: The document mentions "Clinical images collected" and "Clinical images obtained in accordance with Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices." However, the specific sample size of the clinical image test set is not provided.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study seems to be internally conducted by Pixxgen.
• Retrospective or Prospective: Not specified whether the clinical images were collected retrospectively or prospectively.

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

• Number of Experts: "a Board Certified Radiologist" (singular) was used.
• Qualifications: "Board Certified Radiologist." No further detail (e.g., years of experience, subspecialty) is provided.

4. Adjudication Method for the Test Set

• Adjudication Method: "evaluated by a Board Certified Radiologist." This implies a single reader assessment, hence no multi-reader adjudication method (like 2+1 or 3+1) was used or described.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done (or at least, not described in this 510(k) summary). The evaluation was by a single radiologist to confirm "excellent diagnostic quality" of the new device's images, comparing them to the predicate's as a basis for equivalence.
• Effect Size: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Not applicable. This device is a digital X-ray receptor panel, not an AI/ML algorithm. Its performance is assessed as a component producing images for human interpretation, not as an algorithm providing diagnostic outputs independently.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the "clinical image inspection" was expert consensus (from a single Board Certified Radiologist) on the diagnostic quality of the images produced by the device. It was not based on pathology, outcomes data, or a panel of experts.

8. Sample Size for the Training Set

• The document describes the device itself, not an AI/ML algorithm. Therefore, there is no concept of a "training set" for an algorithm. The device's design and engineering are based on established X-ray detector physics and comparison to a predicate device.

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

• Not applicable, as there is no AI/ML algorithm with a training set. The device's "training" in the manufacturing sense involves engineering, quality control, and adherence to performance specifications, not data-driven machine learning.

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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 PIXX series are digital radiography systems, featuring an integrated flat panel digital detector (FPD). They are 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. The modification involves adding battery power capability to the devices in our previous submission K180976.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test set sample size: Not explicitly stated for specific image evaluations. The device modification primarily involved adding battery power, and the manufacturer stated that "Clinical images were not required because the panel technology did not change from K180976."
• Data provenance: Not explicitly stated, as no clinical image study was conducted for this submission. The predicate device (K180976) would have had its own testing data.

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 image study requiring expert review of a test set was conducted for this submission.

4. Adjudication method for the test set:

• Not applicable, as no clinical image study requiring adjudication was conducted for this 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. The submission is for Digital Diagnostic X-ray Receptor Panels, which are imaging hardware, not an AI-enabled diagnostic device.

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

• No standalone algorithm performance study was done or is applicable for this type of hardware device.

7. The type of ground truth used:

• Not applicable for image-based ground truth related to a diagnostic algorithm, as this is a hardware device. The ground truth for this submission was based on bench testing against established engineering and safety standards (e.g., IEC/UL for electrical safety, IEC for EMC, IEC for battery safety) and performance specifications (Pixel Pitch, Limiting Resolution, DQE, MTF, A/D Conversion, etc.) which were determined to be identical to the predicate device. For the new battery feature, battery life testing was conducted to verify its stated performance.

8. The sample size for the training set:

• Not applicable, as this is a hardware device and not an AI/algorithm-based system requiring a training set.

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

• Not applicable, as this is a hardware device and not an AI/algorithm-based system requiring a training set or its associated ground truth establishment.

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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 PIXX series are digital radiography systems, featuring an integrated flat panel digital detector (FPD). They are 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.

The provided document details the 510(k) summary for PIXXGEN Corporation's Digital Diagnostic X-Ray Receptor Panels. It does not describe a study involving an AI/algorithm that meets specific acceptance criteria. Instead, it focuses on the substantial equivalence of new digital X-ray receptor panels (PIXX 1717, PIXX 1417, PIXX 1212) to a legally marketed predicate device (ATAL 9, K152151).

Therefore, I cannot fulfill the request to describe the acceptance criteria and a study proving an AI/algorithm device meets those criteria based on the provided text. The document primarily discusses the performance of the physical imaging devices and their comparison to a predecessor, not the performance of an AI or algorithm itself.

The clinical "study" mentioned is purely for image quality validation by a radiologist against a predicate device, not for assessing an AI's diagnostic performance. There is no mention of a test set, training set, ground truth acquisition methodology (beyond radiologist evaluation), or any of the detailed metrics typically associated with AI/algorithm performance studies.

However, I can extract information related to the device's technical specifications and the "clinical testing" that was done to demonstrate equivalent image quality for the new panels as compared to the predicate device.

Here's a breakdown of what is available in the document, framed as if it were a "device performance" rather than an "AI performance" study:

Acceptance Criteria and Device Performance (for the X-ray Receptor Panels)

The acceptance criteria for these X-ray receptor panels are based on demonstrating "equal or better image quality" compared to the predicate device, alongside meeting various technical and safety standards.

1. Table of Acceptance Criteria and Reported Device Performance (for X-ray Panels):

Note: The document states "some measurements are slightly higher, and some are very slightly lower" for DQE/MTF, but overall concluded "very similar" and within "possible measurement error." The table reflects the reported values.

2. Sample Size and Data Provenance (for "Clinical Testing" of Image Quality):

• Sample Size for Test Set: Not explicitly stated. The document mentions "Clinical images were acquired." It does not specify the number of images or cases.
• Data Provenance: Not specified regarding country of origin. The study was retrospective in nature, as images were "acquired and evaluated." There is no indication of a prospective study design.

3. Number of Experts and Qualifications for Ground Truth:

• Number of Experts: One.
• Qualifications of Experts: "a board certified radiologist." No mention of years of experience.

4. Adjudication Method for the Test Set:

• Adjudication Method: None. A single board-certified radiologist made the evaluation.

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

• MRMC Study: No, an MRMC study was not done. The evaluation was performed by a single radiologist to subjectively compare the image quality of the new panels against the predicate.

6. Standalone Performance (i.e. algorithm only without human-in-the-loop performance):

• This question is not applicable as the document is about physical X-ray receptor panels, not an AI or algorithm. The performance metrics reported (DQE, MTF, Limiting Resolution) are intrinsic to the hardware.

7. Type of Ground Truth Used:

• The "ground truth" for the image quality comparison was established by expert consensus (of a single radiologist), who concluded that "the images from the new panels are as good as or better than the images acquired with the predicate panel." This is a subjective assessment of image quality, not disease presence/absence based on pathology or outcomes data.

8. Sample Size for the Training Set:

• Not applicable. This document describes a medical device (X-ray panels) and does not involve a "training set" for an AI or algorithm. The "clinical images" evaluated were for validation/comparison, not for training.

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

• Not applicable. As there is no training set for an AI, this question is irrelevant to the provided document.

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