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2D Perfusion assists in the diagnosis of perfusions of issues, based on digital subtraction angiography (DSA), by providing color coded images generated from the DSA series.

The 2D Perfusion software medical device is a tool that assists the physician in the diagnosis of perfusion (i.e., passage of blood through blood vessels to an organ or a tissue) alterations by providing a color coded representation of a digital subtraction angiography (DSA) run. The 2D Perfusion software medical device can visualize multiple functional parameters related to the time-density function and also provides a comparison between pre-, peri-, and post-procedural color coded images.

Here's an analysis of the provided text regarding the acceptance criteria and study for the 2D Perfusion device:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain a specific table of quantitative acceptance criteria or corresponding reported device performance metrics. Instead, it describes acceptance criteria in qualitative terms related to compliance with standards and functionality.

• IEC 62304 Medical device software – Software life cycle processes (2006):
• IEC 62366 Application of usability engineering to medical devices (2007):
• ISO 14971 Application of risk management to medical devices (2007)." | "The non-clinical software verification and validation test results demonstrate that the 2D Perfusion software medical device complies with international and FDA-recognized consensus standards..." |
  | Intended Use | "Non-clinical software verification and validation tests have been performed with regards to the intended use..." | "...the 2D Perfusion software medical device ... meets the acceptance criteria and is adequate for its intended use." |
  | Technical Claims | "Non-clinical software verification and validation tests have been performed with regards to... the technical claims..." | "The non-clinical software verification and validation test results demonstrate that the 2D Perfusion software medical device ... meets the acceptance criteria..." (Implying technical claims were met). |
  | Requirement Specifications | "Non-clinical software verification and validation tests have been performed with regards to... the requirement specifications..." | "The non-clinical software verification and validation test results demonstrate that the 2D Perfusion software medical device ... meets the acceptance criteria..." (Implying requirement specifications were met). |
  | Risk Management | "Non-clinical software verification and validation tests have been performed with regards to... the risk management results." | "The non-clinical software verification and validation test results demonstrate that the 2D Perfusion software medical device ... meets the acceptance criteria..." (Implying risk management results were adequately addressed). |
  | Safety & Effectiveness | (Implied through substantial equivalence) "The 2D Perfusion software medical device is substantially equivalent to the currently marketed and secondary predicate AngioViz Option provided with the AW VolumeShare 5 workstation (GE Healthcare, K110834) in terms of design features, fundamental scientific technology, indications for use, and safety and effectiveness." and "...the 2D Perfusion is considered substantially equivalent to the currently marketed and secondary predicate AngioViz Option ... in terms of safety and effectiveness." | "The results of these tests demonstrate that the 2D Perfusion software medical device met the acceptance criteria and is adequate for this intended use." (This indirectly states that it meets the safety and effectiveness criteria by being substantially equivalent to a cleared device and undergoing proper V&V). |

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

The document explicitly states: "The 2D Perfusion software medical device did not require clinical studies since substantial equivalence to the currently marketed and predicate device was demonstrated with the following attributes: ... Non-clinical performance testing."

Therefore, there

• No clinical test set was used.
• The non-clinical performance testing (software verification and validation) would likely involve simulated data or internally generated test cases rather than patient data. The provenance of such data, if used, is not specified.

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

Since no clinical studies or human-reviewed test sets were used for performance evaluation, there were:

• No experts used to establish ground truth for a test set.
• The ground truth for the software verification and validation would be based on predefined expected outputs for given inputs as per the software requirements Specification.

4. Adjudication Method for the Test Set

As no clinical test set with human reviewers was used:

• No adjudication method was employed.

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

• No MRMC comparative effectiveness study was done. The document explicitly states no clinical studies were required. Therefore, no studies comparing human readers with and without AI assistance were conducted or reported.

6. Standalone (Algorithm Only Without Human-in-the-loop Performance) Study

• A standalone (algorithm-only) performance evaluation was conducted. This is evidenced by the "Non-clinical software verification and validation tests," which would assess the software's functionality and accuracy in generating the parametric images as specified. The results of these tests demonstated that the device met the acceptance criteria.

7. Type of Ground Truth Used

For the non-clinical software verification and validation, the ground truth would be:

• Expected software output based on design specifications and engineering principles. This means engineers would have defined what the correct "arrival time," "time to peak," "wash-in rate," etc., should be for a given synthetic or recorded DSA run, and the software's output would be compared against these predefined correct values.

8. Sample Size for the Training Set

The document does not mention a training set. This is because:

• The 2D Perfusion device is described as a tool that "provides a color coded representation of a digital subtraction angiography (DSA) run" and "visualize multiple functional parameters related to the time-density function."
• It is not an AI/ML device in the modern sense that requires a "training set" to learn from data. Instead, it seems to be a deterministic algorithm that calculates parameters based on the time-intensity curve of a DSA input.
• Therefore, no training set was used.

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

As no training set was used:

• No ground truth was established for a training set.

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The XperCT Rel. 3 reconstructs 3D volumes from rotational fluoroscopic acquisitions, and provides CT-like images that assist the physician with diagnosis, surgical planning, interventional procedures, and treatment follow-up. XperCT Rel. 3 helps to manually estimate the dimension of the lesion.

The XperCT Rel. 3 is a software medical device intended to provide high-speed and high resolution 3D cross-sectional imaging in the angiography lab. The XperCT Rel. 3 generates a 3D image that visualizes soft tissue, which helps in identifying anatomy, for example cerebral, abdominal, and peripheral, etc., from a single rotational scan. The XperCT Rel. 3 includes filters to improve the image quality of the reconstruction by reducing the noise caused by metal objects or other objects that absorb high levels of x-ray radiation.

The provided text discusses the XperCT Rel. 3 device, but it does not contain specific acceptance criteria, study details (like sample sizes for test or training sets, data provenance), or performance metrics. The document states that "Non-clinical verification and validation tests were performed... The test results demonstrate that the XperCT Rel. 3 software medical device complies with international recognized standards as detailed in this premarket submission and met the acceptance criteria." However, it does not explicitly list these criteria or their fulfillment.

Therefore, the following table and information can only reflect what is explicitly stated or can be inferred from the provided text, and will highlight where information is missing.

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample sizes used for the test set and the data provenance:

   • Test Set Sample Size: Not specified in the provided text.
   • Data Provenance: Not specified in the provided text. The document refers to "Non-clinical verification and validation tests" but does not indicate the source or nature of the data used in these tests (e.g., country of origin, retrospective or prospective).

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

   • Not specified in the provided text.

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

   • Not specified in the provided text.

4. 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 MRMC comparative effectiveness study is mentioned in the provided text. The device is a software medical device for imaging, which assists physicians, but there is no study described that compares human reader performance with and without its assistance.

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

   • The "Non-clinical verification and validation tests" could imply standalone testing for the software's functionality and image reconstruction capabilities. However, specific details of such a standalone performance study (e.g., metrics, dataset) are not provided. The device is described as assisting the physician, implying it is not intended for fully autonomous use.

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

   • Not specified in the provided text. For image reconstruction, ground truth could refer to ideal reconstructed images or phantom data used for validation.

7. The sample size for the training set:

   • Not specified in the provided text. Information about a training set is not explicitly mentioned as the document focuses on the validation of the device rather than its development or initial training.

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

   • Not specified, as information about a training set or its ground truth is not provided.

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