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This device is a Computed Radiography System and intended for use in producing digital X-Ray images for general radiography purposes. It comprises of scanner, cassette with reusable phosphor storage plate (IP) and workstation software. It scans X-Ray exposed image plate and produces X-Ray image in digital form. Then, digital image is transferred to workstation for further processing and routing. This device is intended to be operated in a radiological environment by qualified staff. This device is not intended for the acquisition of mammographic image data.

The FireCR Spark is Computed Radiography System which produces the X-ray diagnostic image in digital format instead of using traditional screens and film. This device utilizes reusable X-ray storage phosphor plate (IP) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate, X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collect and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

Here's an analysis of the provided text regarding the FireCR Spark Computed Radiography Scanner, focusing on the acceptance criteria and the study proving its performance:

Acceptance Criteria and Device Performance for FireCR Spark (K133120)

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device (FireCR) rather than establishing novel acceptance criteria for a new device. Therefore, the "acceptance criteria" are largely framed as matching or improving upon the predicate device's performance.

The crucial "acceptance criterion" for this 510(k) submission is to demonstrate that the FireCR Spark is substantially equivalent to the predicate device, FireCR, and that any differences do not raise new questions of safety or effectiveness. The device's performance, particularly in DQE and MTF, actually exceeds that of the predicate, which is presented as an enhancement rather than a deviation from acceptance.

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

The document states:

• "In clinical considerations, - The rating was considered equivalent by radiologists."
• "As a result of Clinical Study, FireCR Spark is considered that Image quality is equivalent to the Predicate Device."
• "Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" was performed."

Sample Size for Test Set: The document does not explicitly state the sample size (number of images or patients) used for the clinical study.

Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the submitter's address in Korea, it is possible the clinical data originated there, but this is not confirmed.

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

• Number of Experts: The document states that "The rating was considered equivalent by radiologists." It does not specify the number of radiologists involved.
• Qualifications of Experts: The document identifies them as "radiologists" but does not provide details on their experience level (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the clinical study ratings. It simply states that "The rating was considered equivalent by radiologists."

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

An MRMC comparative effectiveness study was not explicitly stated or performed as described in the provided text. The clinical study aimed to show equivalence in image quality between the FireCR Spark and the predicate device, FireCR, through radiologist ratings, but it does not evaluate the improvement of human readers with AI assistance versus without AI assistance. The device itself is a Computed Radiography Scanner, not an AI-powered diagnostic tool for interpretation assistance.

6. Standalone (Algorithm Only) Performance Study

This section is not applicable as the FireCR Spark is a Computed Radiography Scanner, a hardware device for capturing and digitizing X-ray images, not a standalone algorithm/AI for image interpretation without human interaction. Its performance metrics (DQE, MTF) are intrinsic to the device's image acquisition capabilities.

7. Type of Ground Truth Used

For the clinical study on image quality, the ground truth was based on expert consensus/ratings by radiologists. The document explicitly states: "The rating was considered equivalent by radiologists." and "FireCR Spark is considered that Image quality is equivalent to the Predicate Device."

For the technical performance metrics (DQE, MTF), the "ground truth" would be established through physical measurements and standardized testing methodologies (e.g., per IEC 62220-1).

8. Sample Size for the Training Set

The document describes a hardware device (Computed Radiography Scanner) and its associated acquisition software. It does not mention a "training set" in the context of machine learning or AI. The term "training set" is not relevant to this type of device, which is not an AI algorithm.

9. How Ground Truth for the Training Set Was Established

As there is no mention of a "training set" for an AI algorithm, this question is not applicable to the information provided. The device performs signal processing, digital filtering, gain & offset correction, and flat fielding, which are standard image processing techniques, not machine learning that would require a ground-truthed training set.

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