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The PS 3000 Digital PhotoSpot System " is a high resolution, digital imaging system designed for Digital Videography. It is intended to replace conventional film techniques in multipurpose or dedicated applications where general fluoroscopy, interventional fluoroscopy and angiography or cardiac imaging procedures are performed.

The PS 3000 Digital PhotoSpot System " is indicated for use when it is necessary for a trained health care professional (for example an Radiologist) to acquire, record, review and distribute a digital image from a x-ray image intensifiers in diagnostic imaging chains.

The PS 3000 Digital PhotoSpot System " is a prescription device. The labeling, instructions and user operations are designed for trained professionals.

This device can be described as a Class II diagnostic system that receives an image from an image intensifier tube and acquire, record, display and publish diagnostic images using proprietary techniques. This device is composed of:

• software {that runs in a qualified, ancillary computer}, .
• proprietary hardware and software, and a ●
• CCD Camera that receives the image. .

All ancillary equipment, which works with this device, is identified as a configured item and tested to formal procedures. This device will only be used with specific ancillary equipment, which is tested and qualified to work with PS 3000 Digital PhotoSpot System ***.

The scientific concept on which this device is based that by monitoring images from the image intensified tube a valid diagnostic image can be derived and reproduced.

This device functions by converting an optical (analog) image to a digital image having sufficient diagnostic properties as to assist the physician in establishing a diagnosis.

The intended use of this device is for a trained health care professional to produce a diagnostic inage. The PS 3000 Digital PhotoSpot System " uses sophisticated digital signal processing and data collection/display techniques to offer the physician or trained health care provider, a reliable, simple tool.

The provided text does not contain detailed information about specific acceptance criteria and a study proving device performance in the format requested. The document is a 510(k) summary for a medical device (PS 3000 Digital PhotoSpot System) and primarily focuses on establishing substantial equivalence to a predicate device.

Here's what can be extracted and what is not available based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions "A series of factory adjustments/calibration tests are conducted to verify the device is accurate can maintain calibration over its useful life" and "This device is safe and effective for the application for which it is intended and has been tested to confirm safety and efficacy." However, specific numerical acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or reported performance metrics are not provided. The comparison is made at a high level of technological characteristics and intended use with a predicate device.

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

Not specified. The document mentions "testing" but does not detail any specific test set, its size, or the provenance of the data (country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and their qualifications:

Not applicable/Not specified. Since specific performance studies with human-established ground truth are not detailed, this information is not present. The device is intended for use by a "trained health care professional (for example an Radiologist)," implying human interpretation, but this is about the intended use of the output, not the ground truth for a performance study of the device itself.

4. Adjudication method for the test set:

Not applicable/Not specified. Without a specific test set and ground truth establishment process detailed, adjudication methods are not mentioned.

5. Multi-reader multi-case (MRMC) comparative effectiveness study:

Not mentioned. There is no indication of an MRMC study comparing human readers with and without AI assistance. The document focuses on the device's ability to produce diagnostic images to assist the physician, not on improving human reader performance.

6. Standalone (algorithm only without human-in-the-loop performance) study:

Not explicitly detailed in terms of specific performance metrics. The device itself is "a class II diagnostic system that receives an image from an image intensifier tube and acquire, record, display and publish diagnostic images using proprietary techniques." While it functions independently to process and display images, the document doesn't provide standalone performance metrics typical of an AI algorithm (e.g., sensitivity, specificity for detecting specific conditions). Its "performance" is primarily described as being able to produce "sufficient diagnostic properties as to assist the physician in establishing a diagnosis," and a general statement that "This device is safe and effective for the application for which it is intended."

7. Type of ground truth used:

Not specified. No specific ground truth type (e.g., pathology, expert consensus, outcomes data) is mentioned as part of any performance study. The core claim is that it converts images to digital images with "sufficient diagnostic properties," which implies comparison against existing diagnostic standards, but the exact method for this comparison or "ground truth" is not detailed.

8. Sample size for the training set:

Not applicable/Not specified. The document does not describe the use of machine learning or AI models that would require a "training set" in the modern sense. It refers to "proprietary hardware and software" and "sophisticated digital signal processing and data collection/display techniques," which implies traditional image processing and system design, not a learned model from a data training set.

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

Not applicable/Not specified. As there's no mention of a training set, this information is not relevant to the provided text.

In summary, the provided Exhibit 19 510(k) Summary focuses on demonstrating substantial equivalence to a predicate device based on similar technological characteristics and intended use. It does not contain the detailed performance study information with specific acceptance criteria, ground truth establishment, or study designs (like MRMC or standalone AI performance) as requested.

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