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The flat panel detector CSX-30 is designed to provide fluoroscopic and spot radiographic images of human anatomy during diagnostic, surgical and interventional procedures. Examples of clinical application may include angiography, endoscopy, urologic, orthopedic, neurologic, vascular, critical-care and emergency room procedures or other imaging applications at the physician's discretion. The device is intended to replace spot-film device is also intended to replace fluoroscopic images obtained through image intensifier technology. Not intended for mammography applications.

The CSX-30 is a digital radiography flat panel detector that can take fluoroscopic and spot radiographic images of any part of the body. It directly converts the X-ray images captured by the sensor into high-resolution digital images. The instrument is a component of an x-ray system and as such cannot be used outside of such a system. This unit converts the X-rays into digital signals. Not intended for mammography applications.

The provided text describes a 510(k) summary for a flat panel detector (CSX-30), which is a component of an X-ray system. The study described focuses on demonstrating substantial equivalence to a predicate device (CSX-10) rather than proving "device meets acceptance criteria" in the context of clinical performance or diagnostic accuracy of an AI algorithm.

Therefore, many of the requested criteria for AI/diagnostic studies, such as sample size for test sets, number of experts, adjudication methods, MRMC studies, or specific ground truth methodologies for clinical conditions, are not applicable or detailed in this document because the device is a hardware component (a flat panel detector), not an AI-driven diagnostic system.

The "acceptance criteria" here relate to engineering specifications, safety standards, and performance characteristics compared to a predicate device.

However, I can extract the information that is present and note what is not applicable.

Here's the summary based on the provided document:

Acceptance Criteria and Device Performance Study for CSX-30 Flat Panel Detector

The study aimed to demonstrate substantial equivalence of the new device (CSX-30 Flat Panel Detector) to a legally marketed predicate device (CSX-10 Flat Panel Detector). The "acceptance criteria" are implied by the comparison to the predicate device and compliance with relevant standards.

1. Table of Acceptance Criteria (Implied by Comparison) and Reported Device Performance

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of number of patients or images for clinical performance evaluation, as this is a hardware device submission focusing on engineering and safety. It mentions "non-clinical image comparisons involving flat panel display images." The exact number of images or test runs for these comparisons is not specified.
• Data Provenance: Not explicitly stated regarding country of origin. The study appears to be "non-clinical," focusing on device performance and safety characteristics rather than clinical trial data on specific patient populations. It is retrospective in the sense that a comparison is made to an existing predicate device's characteristics.

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

• Not Applicable/Not Specified: As this is a hardware device submission focused on technical specifications and safety standards, the concept of "ground truth" derived from expert consensus on clinical diagnoses (e.g., by radiologists) is not relevant in the way it would be for an AI diagnostic device. The "truth" is established by direct measurement of device parameters and compliance with engineering standards.

4. Adjudication Method for the Test Set

• Not Applicable: No clinical adjudication process is described as this is not a clinical diagnostic performance study requiring expert consensus on findings.

5. MRMC Comparative Effectiveness Study

• Not Performed/Applicable: An MRMC study is typically for evaluating the impact of an AI system on human reader performance. This study is for a flat panel detector (hardware component), not an AI algorithm.

6. Standalone Performance Study (Algorithm Only)

• Not Applicable: This is not an AI algorithm. The performance of the flat panel detector is assessed through its technical specifications (e.g., pixel count, frame rate, A/D conversion), compliance with safety standards, and non-clinical image quality comparisons with the predicate.

7. Type of Ground Truth Used

• Engineering Specifications and Standard Compliance: The "ground truth" for this device's performance is based on its measured physical and electrical characteristics (e.g., pixel count, dimensions, weight, frame rate, A/D conversion), its ability to meet specified performance parameters (e.g., DOE, dynamic range), and its adherence to established national and international safety and performance standards (e.g., IEC 60601 series, FDA Guidance documents). Comparison of "flat panel display images" implies direct image quality assessment, often against a reference or the predicate.

8. Sample Size for the Training Set

• Not Applicable: This is a hardware device, not an AI model that requires a training set.

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

• Not Applicable: As there is no training set for an AI model, this question is not relevant.

In conclusion, the provided document details a 510(k) submission for a non-AI medical imaging hardware component, focusing on demonstrating substantial equivalence to a predicate device through technical specification comparisons and compliance with relevant safety and performance standards. Many of the questions posed are specifically for AI/software as a medical device (SaMD) clinical performance studies and therefore do not apply to this submission type.

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