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This software is intended to generate digital radiographic images of the skull, spinal column, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, or lying in the prone or supine position and is intended for use in all routine radiography exams. The product is not intended for mammographic applications.

This software is not meant for mammography, fluoroscopy, or angiography.

The I-Q View is a software package to be used with FDA cleared solid-state imaging receptors. It functions as a diagnostic x-ray image acquisition platform and allows these images to be transferred to hard copy, softcopy, and archive devices via DICOM protocol. The flat panel detector is not part of this submission. In the I-Q View software, the Digital Radiography Operator Console (DROC) software allows the following functions:

• 1. Add new patients to the system; enter information about the patient and physician that will be associated with the digital radiographic images.
• 2. Edit existing patient information.
• 3. Emergency registration and edit Emergency settings.
• 4. Pick from a selection of procedures, which defines the series of images to be acquired.
• 5. Adiust technique settings before capturing the x-ray image.
• 6. Preview the image, accept or reject the image entering comments or rejection reasons to the image. Accepted images will be sent to the selected output destinations.
• 7. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
• 8. Close a procedure when all images have been captured.
• 9. Review History images, resend and reprint images.
• 10. Re-exam a completed patient.
• 11. Protect patient records from being deleted by the system.
• 12. Delete an examined Study with all images being captured.
• 13. Edit User accounts.
• 14. Check statistical information.
• 15. Image QC.
• 16. Image stitching.
• 17. Provides electronic transfer of medical image data between medical devices.

The provided document is a 510(k) summary for the I-Q View software. It focuses on demonstrating substantial equivalence to a predicate device through bench testing and comparison of technical characteristics. It explicitly states that clinical testing was not required or performed.

Therefore, I cannot provide details on clinical acceptance criteria or a study proving the device meets them, as such a study was not conducted for this submission. The document relies on bench testing and comparison to a predicate device to establish substantial equivalence.

Here's a breakdown of what can be extracted from the provided text regarding acceptance criteria and the "study" (bench testing) that supports the device:

1. Table of Acceptance Criteria and Reported Device Performance

Since no clinical acceptance criteria or performance metrics are provided, this table will reflect the general statements made about the device performing to specifications.

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

• Test Set Sample Size: Not applicable/not reported. The document describes bench testing, not a test set of patient data.
• Data Provenance: Not applicable. Bench testing generally involves internal testing environments rather than patient data from specific countries or retrospective/prospective studies.

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 test set was used, no experts were needed to establish ground truth for patient data. Bench testing typically relies on engineering specifications and verification.

4. Adjudication method for the test set

• Not applicable. No clinical test set or human interpretation was involved.

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, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical Testing: The bench testing is significant enough to demonstrate that the I-Q View software is as good as the predicate software. All features and functionality have been tested and all specifications have been met. Therefore, it is our conclusion that clinical testing is not required to show substantial equivalence." The device is software for image acquisition, not an AI-assisted diagnostic tool.

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

• Yes, in a sense. The "study" described is bench testing of the software's functionality and its integration with solid-state detectors. This is an evaluation of the algorithm/software itself.

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

• For bench testing, the "ground truth" would be the engineering specifications and expected functional behavior of the software and its interaction with hardware components. It's about verifying that the software performs according to its design requirements.

8. The sample size for the training set

• Not applicable. The I-Q View is described as an image acquisition and processing software, not an AI/machine learning model that typically requires a training set of data.

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

• Not applicable, as there is no mention of a training set or AI/machine learning component.

Summary of the "Study" (Bench Testing) for K203703:

The "study" conducted for the I-Q View software was bench testing. This involved:

• Verification and validation of the software.
• Demonstrating the intended functions and relative performance of the software.
• Integration testing to verify that compatible solid-state detectors performed within specification as intended when used with the I-Q View software.

The conclusion drawn from this bench testing was that the software performs to specification and is "as safe and as functionally effective as the predicate software." This was deemed sufficient to demonstrate substantial equivalence, and clinical testing was explicitly stated as not required.

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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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