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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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XperCT is a software option on the Allura Xper product family intended for imaging bone, soft tissue and other body structures. It reconstructs 3D volumes from rotational fluoroscopy acquisitions, and provides CT-like images to assist the physician in diagnosis, surgical planning, interventional procedures and treatment follow-up.

XperCT is a software option on the Allura Xper product family. It reconstructs 3D volumes from rotational fluoroscopy acquisitions, and provides CT-like images.

The provided 510(k) summary for Philips Medical Systems Nederland B.V.'s XperCT software option (K060749) does not contain information about specific acceptance criteria, a detailed study proving the device meets those criteria, or most of the requested study design parameters.

The submission is a summary of substantial equivalence to a predicate device (DynaCT by Siemens, K042646) rather than a detailed report of a performance study with defined acceptance criteria. The basis for substantial equivalence is stated as:

• XperCT does not introduce new indications for use.
• XperCT has the same technological characteristics as the predicate device.
• XperCT does not introduce new potential hazards or safety risks.

Therefore, most of the requested information regarding acceptance criteria, study design, and performance metrics cannot be extracted from the provided text.

Here's a breakdown of what can and cannot be answered based on the provided document:

1. A table of acceptance criteria and the reported device performance

• Cannot be provided. The document does not specify any quantitative acceptance criteria or report performance metrics from a specific study (e.g., sensitivity, specificity, accuracy). Its clearance is based on substantial equivalence to a predicate device with similar technological characteristics and intended use.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Cannot be provided. The document does not describe a performance study with a distinct test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Cannot be provided. No information on ground truth establishment for a test set is available.

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

• Cannot be provided. No information on adjudication methods for a test set is available.

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

• Cannot be provided. The document does not mention any MRMC study or evaluate the improvement of human readers with AI assistance. XperCT is described as a software option that reconstructs 3D volumes and provides CT-like images, not necessarily an AI-assisted diagnostic tool in the sense of improving human reader performance.

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

• Cannot be provided. The document describes the device's function (reconstructing 3D volumes) but does not detail a standalone performance evaluation in terms of diagnostic effectiveness. Its clearance is based on similarity to a predicate device, implying its performance is expected to be comparable without needing an explicit standalone clinical study description in this summary.

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

• Cannot be provided. No performance study details are available to infer the type of ground truth used.

8. The sample size for the training set

• Cannot be provided. The document does not describe the development or training of an AI algorithm in a way that would involve a training set. The device is a reconstruction software.

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

• Cannot be provided. As above, no training set or ground truth establishment for training is discussed.

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