Search Results

The GR17 is a selenium-based direct conversion DR detector intended for use by a qualified/trained doctor or technician and is designed to perform radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general purpose diagnostic procedures. The GR17 is not used for mammography.

The GR17 is a 17" x 17" Flat Panel Digital Radiographic Detector for General Radiographic Use. It uses amorphous Selenium (a-Se) as the primary photoconductor.

The provided text is a 510(k) summary for the ANRAD Corporation GR17 Digital Detector, which is an X-ray imager. The submission is for a "Modification to specification for defective pixel detection and correction."

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions a modification to the algorithm for detecting bad pixels and refers to "Pixel defect correction Comparison tests and Results are provided in APPENDIX E." However, the specific acceptance criteria values and the reported device performance in relation to those criteria for the defective pixel detection and correction algorithm are not explicitly stated in the provided text. The text only states that the "Verification Tests were chosen to ensure test coverage of the areas changed in the specifications for pixel defects."

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only refers to "Verification Tests" and "Comparison tests" related to pixel defect correction. These tests would likely involve various X-ray images with simulated or real pixel defects.

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

The document does not mention the use of human experts to establish ground truth for the test set regarding pixel defect detection and correction. For technical performance metrics like pixel defects, it is more common for ground truth to be established through objective measurements and technical specifications rather than human expert interpretation of images.

4. Adjudication Method for the Test Set:

Given that the ground truth for pixel defects is typically established through objective technical means, an adjudication method like 2+1 or 3+1 involving human readers is not applicable or mentioned in this context.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned in the provided text. This type of study is more relevant for evaluating diagnostic accuracy with human readers, which is not the focus of this particular modification related to pixel defect correction. The submission is for a technical modification rather than a diagnostic performance claim.

6. Standalone (Algorithm Only) Performance Study:

A standalone performance study for the pixel defect detection and correction algorithm was implicitly performed. The text states:

• "The particular verification tests were chosen to ensure test coverage of the areas changed in the specifications for pixel defects."
• "Pixel defect correction Comparison tests and Results are provided in APPENDIX E."

This indicates that the algorithm's performance in identifying and correcting pixel defects was evaluated on its own against specified technical criteria. The results of this standalone evaluation are expected in Appendix E, which is not provided here.

7. Type of Ground Truth Used:

The ground truth used for evaluating the pixel defect detection and correction algorithm would be technical specifications and objective measurements of pixel defects. This would involve:

• Deliberately introduced or simulated "defective" pixels.
• Pre-defined criteria for what constitutes a "defective pixel" (e.g., dead pixels, stuck-on pixels, clusters).
• Objective methods to verify if the algorithm correctly identifies these defects and effectively applies corrections without introducing artifacts.

8. Sample Size for the Training Set:

The document does not provide any information about a training set or its sample size. This type of submission, focusing on a modification to a pixel correction algorithm, may not require a separate "training set" in the traditional machine learning sense, especially for algorithms based on predefined rules or thresholds rather than complex learned features. If the algorithm uses machine learning, the training data would be specific to pixel patterns, not necessarily patient images.

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

Since no training set information is provided, how its ground truth was established is not discussed or applicable based on the provided text.

Ask a specific question about this device

The GR17 is an amorphous Selenium-based direct conversion Digital Radiography (DR) detector intended for use by a qualified/trained doctor or technician and is designed to generate radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general purpose diagnostic procedures.

The GR17 is a 17 inch by 17 inch digital delector. It is intended to convert X-rays into efectrical signals to create usable images for diagnostic use.

The provided text is a 510(k) summary for the ANRAD CORPORATION GR17 Digital Detector, submitted in 2004. It describes the device, its intended use, and claims substantial equivalence to predicate devices based on clinical and non-clinical testing. However, the document does not provide specific acceptance criteria or detailed results from the studies that prove the device meets such criteria.

Therefore, I cannot populate the requested table and answer many of the questions as the information is not present in the provided text.

Here's what can be extracted and what cannot:

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

• Cannot Populate. The 510(k) summary states "Based on the Clinical Study Report dated September 8, 2004, the GR17 Digital Detector is substantially equivalent to the predicate device." and "The testing of the GR17 Digital Detector demonstrates that the performance is substantially equivalent to the predicate devices cited above." However, it does not define specific acceptance criteria (e.g., a certain sensitivity/specificity, SNR, MTF values) nor does it report specific performance metrics against any such criteria. It only asserts substantial equivalence.

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

• Cannot Populate. The document mentions a "Clinical Study Report dated September 8, 2004" but does not provide any details about the sample size, type of study (retrospective/prospective), or data provenance.

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)

• Cannot Populate. This information is not present in the provided text.

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

• Cannot Populate. This information is not present in the provided text.

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

• Cannot Populate. This information is not present in the provided text. The device is a "Solid State X-Ray Imager (Flat Panel / Digital Imager)", meaning it's a hardware detector, not an AI-powered diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance would not be applicable to this device type. The document states it is "intended to replace radiographic film/screen systems," implying a comparison to traditional film.

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

• Cannot Populate. The device is a hardware detector, not an algorithm. Standalone performance for an algorithm is not applicable here. The "performance" mentioned would refer to the image quality characteristics of the detector itself.

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

• Cannot Populate. This information is not present in the provided text.

8. The sample size for the training set

• Cannot Populate. This information is not present in the provided text. As this is a hardware device (detector), the concept of a "training set" in the context of machine learning is not directly applicable. If it refers to data used to optimize hardware parameters, that information is not provided.

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

• Cannot Populate. (See point 8).

Ask a specific question about this device