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syngo® Dual Energy is designed to operate with Siemens Dual Source CT scanners. CT images taken at the same time, using two different kV levels, of the same anatomical region of a patient are used. Depending on the region of interest, contrast agents may be used. The differences in the energy dependence of the attenuation coefficient of the different materials provide information about the chemical composition of body materials. The images are combined to visualize and analyze information about anatomical and pathological structures.

The functionality of the syngo® Dual Energy applications are as follows:

• Lung Nodules
• Xenon (Lung Ventilation)
• Monoenergetic
• Brain Haemorrhage
• Gout Evaluation
• Lung Vessels
• Heart PBV
• Dual Energy Bone Removal
• Cartilage, Tendon, Ligament
• Lung Perfusion
• Liver
• Hard Plaques in Vessels
• Kidney Stones

Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid and non-uric acid stones. For full identification of the kidney stone additional clinical information should be considered such as patient history and urine testing. Only a well-trained radiologist can make the final diagnosis under consideration of all available information. The accuracy of identification is decreased in obese patients.

syngo® Dual Energy Software Package is a post processing application package consisting of the following applications that can be used to improve visualization of various energy dependent materials in the human body:

• . Lung Nodules
• Lung Nodules
  Xenon (Lung Ventilation) .
• Monoenergetic .
• Brain Haemorrhage .
• Gout Evaluation .
• Luna Vessels .
• Heart PBV .
• . Dual Energy Bone Removal
• Cartilage, Tendon, Ligament .
• Lung Perfusion .
• Liver ●
• Hard Plaques in Vessels .
• Kidney Stones .

The output of the syngo® Dual Energy Software Package applications are shown within three orthogonal slices (sagittal, coronal, and axial) as overlay to anatomical grayscale information. Typically the anatomical information is built from a combination of the original dual energy scans (for example 80kV and 140kV or 100 kV and 140kV and/or by using different filters).

Window values, color look-up tables (LUTs) and masking operations for both datasets (anatomical and the material decomposition information) can be interactively defined and user modified. An additional side-by-side display of Dual Energy results is also supported, and navigation and rotation of datasets are provided.

After two series of dual energy images have been loaded from the patient browser (80kV and 140kV or 100 kV and 140kV), three orthogonal slices (sagittal, coronal, and axial) of both spectra are shown within the respective segments on the monitor in grayscale mode. The middle axial, sagittal and coronal view is used for the initial display.

The Kidney Stones application class is a post processing application that uses dual energy information to visualize the chemical composition in kidney stones. The basis for this approach is a material decomposition into tissue, uric acid, and oxalate stones. The result of material decomposition is shown as a color overlay to anatomical gray scale information.

Here's a breakdown of the acceptance criteria and the study details for the syngo® Dual Energy Software Package, specifically concerning the Kidney Stones application class, based on the provided text:

Acceptance Criteria and Device Performance

The provided document describes the syngo® Dual Energy Software Package as a post-processing application for CT images, with a specific application for Kidney Stones designed to visualize the chemical composition and differentiate between uric acid and non-uric acid stones.

Acceptance Criteria:

The document states that "The testing results supports that all the software specifications have met the acceptance criteria." However, it does not explicitly list quantitative acceptance criteria for the Kidney Stones application's performance (e.g., specific accuracy, sensitivity, or specificity thresholds). Instead, it describes the type of testing performed and the expected outcomes of the software.

Reported Device Performance:

No specific quantitative performance metrics (e.g., accuracy, sensitivity, specificity) for the Kidney Stones application are reported in the provided text. The document focuses on establishing substantial equivalence to previously cleared devices rather than providing detailed performance data for the subject device.

Table of Acceptance Criteria and Reported Device Performance:

Study Details

The provided text describes non-clinical testing for the Kidney Stones application class during product development.

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

   • Sample Size: Not explicitly stated as a number of phantoms or stones. It mentions "kidney stones of various chemical composition and sizes" were used.
   • Data Provenance: Retrospective or Prospective is not specified. The data was generated through "Phantom based test" which were "conducted during product development." This implies a controlled, likely prospective, experimental setup using phantoms. No country of origin for data generation is specified beyond the general manufacturing site in Germany for Siemens AG Medical Solutions.

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

   • Not applicable for this phantom-based test. The ground truth was inherent in the known chemical composition and size of the kidney stones placed in the phantoms.

3. Adjudication method for the test set:

   • Not applicable. Ground truth was established by the known composition of the phantom stones, not by expert adjudication.

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 study was mentioned. The study described is a non-clinical, phantom-based validation of the software's ability to classify stone composition. It does not involve human readers or assess improvement with AI assistance.

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

   • Yes, the described "Phantom based test" appears to be a standalone test of the algorithm's performance in classifying stone composition given known samples. "Classification performance was measured as a function of CTDIval."

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

   • Known chemical composition and size of kidney stones within phantoms. This is a highly controlled, synthetic ground truth, not derived from clinical expert consensus, pathology, or outcomes data from patients.

7. The sample size for the training set:

   • Not specified. The document only covers validation testing, not the specifics of the training data.

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

   • Not specified. The document does not describe the training process or the ground truth establishment for any training data.

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