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The XRD 1622 AP3 MED, when used with a radiographic imaging system, is indicated for use in generating radiographic images of human anatomy for diagnostic X-ray procedures, wherever conventional screen-film (SF), digital radiography (DR), or computed radiography (CR) systems may be used. It is not intended for mammographic use.

The XRD 1622 AP3 MED is a flat panel x-ray detector consisting of an amorphous silicon panel with a directly deposited CsI:Tl scintillator. The XRD 1622 AP3 MED detector has an active area of 41cm x 41cm at a pixel pitch of 200μm. Data and control communication is accomplished via a Gigabit Ethernet interface. The detector can be integrated into a fixed room x-ray system to enable digital radiography.

Here's an analysis of the provided text, focusing on the acceptance criteria and study information for the PerkinElmer XRD 1622 AP3 MED Flat Panel Detector:

Acceptance Criteria and Device Performance Study

The primary study presented aims to demonstrate substantial equivalence to a predicate device, focusing on "equivalent diagnostic quality" of the generated images.

1. Acceptance Criteria and Reported Device Performance

Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than setting explicit quantifiable performance metrics as "acceptance criteria" in the same way a de novo submission might. The key acceptance criterion here is the subjective assessment of "equivalent diagnostic quality."

2. Sample Size and Data Provenance for the Test Set

• Sample Size for Test Set: 30 image pairs.
• Data Provenance: "external testing in actual user testing facility." This implies prospective data collection, possibly within a clinical environment, specifically for the purpose of this evaluation. The country of origin is not explicitly stated.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three.
• Qualifications of Experts: "board certified radiologists." The number of years of experience is not specified.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated. It mentions the images were "reviewed by three board certified radiologists," but it doesn't detail how their opinions were combined or resolved if there were disagreements (e.g., 2+1, 3+1, majority vote, etc.). It simply concludes they "found to produce images of equivalent diagnostic quality."

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

• Was an MRMC study done? Not explicitly in the sense of comparing human readers with vs. without AI assistance. The study described is a comparison of two different imaging devices (the new device vs. the predicate device), with human readers assessing the image quality from both. It evaluates the diagnostic equivalence of the devices, not the improvement of human readers with AI assistance.
• Effect Size of Human Readers' Improvement with AI vs. without AI assistance: Not applicable, as this type of study was not conducted or reported.

6. Standalone (Algorithm Only) Performance Study

This device is a hardware component (Flat Panel Detector), not an AI algorithm. Therefore, a "standalone algorithm only" performance study is not applicable. The device's performance is intrinsically linked to its ability to capture images that can be interpreted by humans.

7. Type of Ground Truth Used

The ground truth for the comparison was expert consensus / subjective evaluation of diagnostic quality by three board-certified radiologists. They assessed the "equivalent diagnostic quality" of images produced by the new device compared to the predicate device.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a hardware component (a flat panel detector), not an AI-driven software or algorithm that requires a "training set" in the conventional machine learning sense. The device's design and engineering are based on established physics and imaging principles, not trained on a dataset.

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

• Ground Truth for Training Set Establishment: Not applicable for the same reasons as #8. The "training" for such a device is its engineering, calibration, and adherence to physical principles and manufacturing specifications.

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The Definium™ 5000 X-Ray Systems are 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 Definium 5000 Digital Radiographic Imaging System provides state of the art image quality, image manipulation, operator control, dose reporting and system maintenance. These features make this system easy to use and reliable while providing high quality radiographic images in a digital environment. The Definium 5000 System is designed to handle radiographic applications using the digital table. The system configuration includes an elevating or non-clevating table, a floor column stand with rotating U-arm, and GE's patented Digital Detector that captures radiographic images in digital form, as well as an X-ray generator/power unit. An Acquisition and Review Workstation for image post-processing, short-term storage, and quick in-room viewing of images is included as part of the system. Images may be transferred manually or automatically via a DICOM network for printing, longterm storage archive, and detailed review.

The provided text is a 510(k) summary for the Definium™ 5000 X-Ray System (K063283). This document aims to demonstrate substantial equivalence to previously cleared devices rather than providing a detailed study proving performance against acceptance criteria.

Therefore, many of the requested details about acceptance criteria, specific study designs, sample sizes, and expert qualifications are not present in the provided text. The document focuses on comparing the new device's characteristics to its predicate devices to establish substantial equivalence.

Here's an attempt to answer the questions based only on the provided information:

1. A table of acceptance criteria and the reported device performance

The provided document does not explicitly state quantitative acceptance criteria for image quality or clinical performance as it would in a de novo submission or a detailed clinical study report. Instead, it attests to "state of the art image quality" and that "the new device is as safe and effective as the predicate devices" based on "bench, user, and standards testing."

The "Substantial Equivalence Chart" compares technical characteristics rather than performance metrics against acceptance criteria.

Note: The differences in "Digital Panel Size" and "Digital Resolution" are noted in the submission but are deemed not to raise new questions of safety or effectiveness, as the overall system is considered substantially equivalent. The performance "criteria" are implicitly met if the device is deemed substantially equivalent.

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 "bench, user, and standards testing" but does not provide any details on sample sizes, whether the data was retrospective or prospective, or the country of origin.

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)

This information is not provided in the document.

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

This information is not provided in the document.

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

This device is an X-ray system, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and was not performed. The document is about the physical X-ray system.

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

This refers to an AI algorithm's performance. As the device is an X-ray system and not an AI algorithm, this type of standalone performance study is not applicable and was not performed.

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

The document refers to "bench, user, and standards testing" leading to a conclusion of safety and effectiveness, but it does not specify the type of ground truth used. For an X-ray system, ground truth for image quality might involve physical phantoms, dose measurements, or qualitative expert assessment of image clarity, but these details are not provided.

8. The sample size for the training set

This refers to AI algorithm training. As the device is an X-ray system and not an AI algorithm, a "training set" as commonly referred to in AI/ML is not applicable.

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

Not applicable, as this is not an AI/ML device.

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