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syngo.CT Applications is a set of software applications for advanced visualization, measurement, and evaluation for specific body regions.

This software package is designed to support the radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice e.g. in the:

• · Evaluation of perfusion of organs and tumors and myocardial tissue perfusion
• · Evaluation of bone structures and detection of bone lesions
• · Evaluation of CT images of the heart
• · Evaluation of the coronary lesions
• · Evaluation of the mandible and maxilla
• · Evaluation of dynamic vessels and extended phase handling

· Evaluation of the liver and its intrahepatic vessel structures to identify the vascular territories of sub-vessel systems in the liver

• Evaluation of neurovascular structures
• · Evaluation of the lung parenchyma
• · Evaluation of non-enhanced Head CT images
• · Evaluation of vascular lesions

The syngo.CT Applications are syngo based post-processing software applications to be used for viewing and evaluating CT images provided by a CT diagnostic device and enabling structured evaluation of CT images.

The syngo.CT Applications is a combination of fourteen (14) medical devices which are handled as features / functionalities within syngo.CT Applications.

The provided text is a 510(k) summary for the device "syngo.CT Applications." It describes the device, its indications for use, and a comparison to a predicate device. However, it does not explicitly detail the acceptance criteria for the device's performance nor does it present a study that proves the device meets specific performance metrics.

Instead, the document primarily focuses on:

• Substantial Equivalence: Arguing that the new version of syngo.CT Applications is substantially equivalent to a previously cleared version and other reference devices.
• Software Verification and Validation: Stating that V&V activities were performed and that the device conforms to special controls and standards.
• Risk Analysis: Confirming risk analysis was completed and controls implemented.
• Compliance with Standards: Listing recognized consensus standards the device meets.

Therefore, many of the requested details about acceptance criteria and a specific performance study are not available in the provided text.

Here's an attempt to answer based on the information available and what can be inferred:

Acceptance Criteria and Device Performance Study Information

Disclaimer: The provided document is a 510(k) summary, primarily focused on demonstrating substantial equivalence to a predicate device, rather than a detailed report of a performance study with specific acceptance criteria and results. Therefore, much of the requested information regarding specific quantitative acceptance criteria and the details of a primary performance study is not explicitly stated in the text. The document refers to "testing supports that all software specifications have met the acceptance criteria," but does not list these criteria or detailed results.

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned above, specific quantitative acceptance criteria and their corresponding reported performance values are not detailed in the provided 510(k) summary. The document broadly states:

• "The testing supports that all software specifications have met the acceptance criteria."
• "The testing results support that all the software specifications have met the acceptance criteria."
• "The result of all testing conducted was found acceptable to support the claim of substantial equivalence."

This indicates that internal acceptance criteria were established and met, but their specifics are not published here.

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

The document does not specify the sample size used for any test set or the provenance (e.g., country of origin, retrospective/prospective) of data used for testing. It refers to "testing for verification and validation" but does not provide these details.

3. Number of Experts and Qualifications for Ground Truth

The document does not provide any information regarding the number of experts used to establish ground truth for a test set, nor their qualifications. Given the nature of the device (advanced visualization and measurement tools), it is likely that such evaluation involved clinical experts, but this is not stated.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for establishing ground truth or evaluating the test set.

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

The document does not mention that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, nor does it provide any effect size regarding human reader improvement with or without AI assistance. The submission focuses on software changes and bundling previously cleared functionalities.

6. Standalone (Algorithm Only) Performance Study

The document primarily describes a software application that assists radiologists and physicians. While it refers to "software verification and validation," it does not explicitly describe a standalone (algorithm only without human-in-the-loop performance) study for any specific AI or image processing component with quantitative results. The functions described are tools for evaluation by a human user.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any testing. Given the nature of medical imaging software, potential ground truth sources could include:

• Expert consensus (e.g., radiologists, cardiologists)
• Pathology reports
• Clinical outcomes
• Reference standards or phantoms
  However, the document does not specify which, if any, were used.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set. This information is typically relevant for machine learning-based algorithms, and while syngo.CT Pulmo 3D mentions a "lung lobe segmentation algorithm," and syngo.CT CaScoring involves calculations, the document doesn't delve into the specifics of their underlying models or training.

9. How Ground Truth for the Training Set Was Established

The document does not provide any information on how ground truth for the training set was established.

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syngo.CT Dual Energy is designed to operate with CT images based on two different X-ray spectra.

The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. syngo.CT Dual Energy combines images acquired with low and high energy spectra to visualize this information. Depending on the region of interest, contrast agents may be used.

The general functionality of the syngo.CT Dual Energy application is as follows:

• · Monoenergetic 1)
• · Brain Hemorrhage
• · Gout Evaluation 1)
• · Lung Vessels
• · Heart PBV
• · Bone Removal
• · Lung Perfusion
• · Liver VNC 1)
• · Monoenergetic Plus 1)
• · Virtual Unenhanced 1)
• Bone Marrow
• · Hard Plaques
• Rho/Z
• · Kidney Stones 1) 2)
• · SPR (Stopping Power Ratio)
• · SPP (Spectral Post-Processing Format) 1)
• · Optimum Contrast 1)

The availability of each feature depends on the Dual Energy scan mode.

1. This functionality supports data from Photon-Counting CT scanners.

2. Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between 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 upon consideration of all available information. The accuracy of identification is decreased in obese patients.

Dual energy offers functions for qualitative and quantitative post-processing evaluations. syngo.CT Dual Energy is a post-processing application consisting of several post-processing application classes that can be used to improve the visualization of the chemical composition of various energy dependent materials in the human body when compared to single energy CT. Depending on the organ of interest, the user can select and modify different application classes or parameters and algorithms.

Different body regions require specific tools that allow the correct evaluation of data sets. syngo.CT Dual Energy provides a range of application classes that meet the requirements of each evaluation type. The different application classes for the subject device can be combined into one workflow.

The provided text is a 510(k) summary for the syngo.CT Dual Energy device, specifically addressing modifications that enable its application classes (Liver VNC, Kidney Stones, Gout Evaluation) to support Photon-Counting CT (PCCT) data. While it discusses performance evaluations, it does not present a formal study with acceptance criteria and detailed quantitative results in the format requested.

The document indicates that the acceptance criteria for these modifications were primarily based on ensuring the existing algorithms and functionality, when applied to PCCT data, yield comparable results to their performance with previously approved dual-source dual-energy (DSDE) data or true non-contrast images, as appropriate. The evaluation appears to be a consistency check rather than a comparative effectiveness study against human readers or a standalone performance study with strict statistical endpoints.

Based on the provided text, here's an attempt to answer the questions, highlighting what information is available and what is not:

Acceptance Criteria and Device Performance Study for syngo.CT Dual Energy (K232155 - PCCT data support)

The provided submission summarizes the non-clinical testing performed to demonstrate that the updated syngo.CT Dual Energy, with its new support for Photon-Counting CT (PCCT) data for Liver VNC, Kidney Stones, and Gout Evaluation, continues to perform as intended and is substantially equivalent to its predicate. The "acceptance criteria" are implied by the performance evaluation statements, focusing on agreement and similarity of results between PCCT data and established methods (DSDE data or true non-contrast images).

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

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

• Liver VNC: "four-phase liver scans"
• Kidney Stones: "phantom data" (unspecified quantity) and "clinical data" (unspecified quantity)
• Gout Evaluation: Unspecified quantity of data for direct comparison.

Data Provenance:
The data provenance is not explicitly stated in terms of country of origin or whether it was retrospective or prospective. However, the mention of "NAEOTOM Alpha" (a Siemens PCCT scanner) suggests it's likely proprietary or internal test data generated for validation purposes. The phrase "already approved" for DSDE data suggests existing, previously validated clinical data might have been used for comparison.

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

This information is not provided in the document. The evaluations described ("agreed well," "similar to," "were the same") imply a comparison method, but the human reference or "ground truth" establishment process for these specific tests is not detailed, nor are the number and qualifications of experts involved.

4. Adjudication method for the test set

This information is not provided in the document. Given the summary nature of the performance data, it's unlikely a formal adjudication process for establishing ground truth for these specific validation tests would be detailed here.

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

No, a MRMC comparative effectiveness study involving human readers assisting with AI assistance versus without AI assistance was not mentioned or described in this 510(k) summary. The study focuses on the device's performance with new data types (PCCT), not on its impact on human reader performance.

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

Yes, the described performance data appears to be standalone (algorithm only), focusing on the processing capabilities of the syngo.CT Dual Energy software with PCCT data and comparing its output to known values (phantoms) or established results (DSDE data, true non-contrast images). There is no mention of human interaction being part of the evaluation of the algorithm's performance in these specific tests.

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

• Liver VNC: "True non contrast images" served as the ground truth/reference.
• Kidney Stones:
  • For phantom data: "known size and composition of the stones in the phantoms" served as ground truth.
  • For clinical data: "already approved dual-source dual-energy (DSDE) data" likely served as the reference for "similar" performance.
• Gout Evaluation: "results from the already approved DSDE scan mode" served as the reference for comparison.

8. The sample size for the training set

The document does not provide any information about the training set size for the algorithms within syngo.CT Dual Energy. Given that the 510(k) is for modifications to support new data types (PCCT) for existing algorithms, it's implied that the core algorithms were developed and trained previously. This submission specifically addresses the validation of these existing algorithms on the new PCCT data.

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

The document does not provide any information on how the ground truth for the training set (from the original algorithm development) was established.

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AI-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the lungs.

It provides the following functionality:

· Segmentation and measurements of complete lung and lung lobes

• · Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
• · Providing an interface to external Medical Device syngo.CT Lung CAD
• · Segmentation and measurements of solid lung nodules
• · Dedication of found lung nodules to corresponding lung lobe
• · Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data.
• · Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinguished.

The software has been validated for data from Siemens (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

Only DICOM images of adult patients are considered to be valid input.

AI-Rad Companion (Pulmonary) SW version VA20 is an enhancement to the previously cleared device AI-Rad Companion (Pulmonary) K183271 that utilizes machine and deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax.

As an update to the previously cleared device, the following modifications have been made:

• Modified Indications for Use Statement The indications for use statement was updated to include descriptive text for the lung lesion follow feature.
• Updated Subject Device Claims List The claims list was updated to include claim pertaining to the lung lesion follow up feature.
• Lung Lesion Follow-up Assessment of current and prior lesions This feature provides the possibility to compare currently segmented lung lesions with corresponding priors and changes to the correlated data are assessed quantitatively.
• Pulmonary Density Assessment

This feature provides the possibility to segment opacity regions inside the lung using an AI algorithm. AI-Rad Companion (Pulmonary) counts image voxels inside opacity regions and calculates the percentages of these voxels relative to the total number of voxels per lobe. lung and in total. Afterwards, each of the five lung lobes is assigned a score ranging from 0 to 4 based on the percentage of opacity as follows: 0 (0%), 1 (1%-25%), 2 (26%-50%), 3 (51%-75%), or 4 (76%-100%). Then a summation of the five lobe scores (range of possible scores, 0-20) are generated in the device outputs. This functionality is commercially available on the Siemens syngo.CT Extended Functionality (K203699).

• . Bi-Directional Lesion Diameter
  This feature provides an additional measurement derived from the existing segmentation contour of a lung lesion. The existing list of measurements is extended with the maximum orthogonal diameter in 2D (short axis diameter) which is orthogonal to the lesion's maximum 2D diameter (2D diameter, long axis diameter). This functionality is commercially available on the Siemens syngo.CT Extended Functionality (K203699).

• . Architecture Enhancement for on premise Edge deployment

  • The system supports the existing cloud deployment as well as an on premise "edge" deployment. The system remains hosted in the teamplay digital health platform and remains driven by the AI-Rad Companion Engine. Now the edge deployment implies that the processing of clinical data and the generation of results can be performed onpremises within the customer network. The edge system is fully connected to the cloud for monitoring and maintenance of the system from remote.

The provided document, a 510(k) summary for Siemens Healthcare GmBh's AI-Rad Companion (Pulmonary) SW version VA20, describes the device, its intended use, and the non-clinical tests performed to demonstrate its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based solely on the provided text:

Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for all functionalities. However, it does state some performance metrics for one specific feature:

Missing Information: The document does not provide acceptance criteria or performance results for other key functionalities of the AI-Rad Companion (Pulmonary), such as:

• Segmentation and measurements of complete lung and lung lobes.
• Identification of areas with lower Hounsfield values.
• Segmentation and measurements of solid lung nodules.
• Dedication of found lung nodules to corresponding lung lobe.
• Identification of areas with elevated Hounsfield values (Pulmonary Density Assessment).
• Bi-directional lesion diameter measurements.

Study Details:

The provided document describes a non-clinical bench test specifically for the lesion follow-up feature. It explicitly states: "No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion (Pulmonary)." This implies that the reported performance metrics are from an algorithm-only (standalone) performance evaluation, without human-in-the-loop.

Here's what can be extracted about the study:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 200 cases were used to evaluate the lesion follow-up feature.
   • Data Provenance: Not explicitly stated. The document mentions validation for data from Siemens, GE Healthcare, and Philips (reconstruction types specified), but it does not specify the country of origin of the data or whether it was retrospective or prospective.

2. Number of Experts and Qualifications:

   • The document does not provide information on the number of experts used to establish ground truth or their specific qualifications for the test set.

3. Adjudication Method:

   • The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set. Since it's a non-clinical bench test of the algorithm's ability to identify lesion pairs, it's possible that a different form of ground truth establishment (e.g., based on established physical measurements or derived from existing clinical reports) was used rather than direct expert consensus on each case.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC study was done. The document explicitly states: "No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion (Pulmonary)." Therefore, there is no effect size reported for human readers improving with AI vs. without AI assistance.

5. Standalone (Algorithm Only) Performance:

   • Yes, a standalone study was done for the lesion follow-up feature. The reported sensitivity and PPV are for the algorithm's performance in identifying lesion pairs.

6. Type of Ground Truth Used:

   • The document does not explicitly state the type of ground truth used for the lesion follow-up test. It mentions "evaluation of 200 cases to identify lesion pairs," which suggests that a definitive ground truth for paired lesions was available for these 200 cases. This could range from expert consensus, to prior established measurements, or structured clinical reports that define lesion pairs.

7. Training Set Sample Size:

   • The document does not specify the sample size used for the training set. It only mentions the use of "machine and deep learning algorithms."

8. How Ground Truth for Training Set Was Established:

   • The document does not describe how the ground truth for the training set was established.

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Al-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the lungs. It provides the following functionality:

· Segmentation and measurements of complete lung and lung lobes

· Identification of areas with lower Hounsfield values in comparison to a predetined threshold for complete lung and lung lobes

· Providing an interface to external Medical Device syngo.CT Lung CAD

· Segmentation and measurements of found lung lesions and dedication to corresponding lung lobe.

· Identification of areas with elevated Hounsfield values. where areas with elevated versus high opacities are distinguished.

The software has been validated for data from Siemens Healthineers (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

Only DICOM images of adult patients are considered to be valid input.

Al-Rad Companion is a software only medical system that investigates data from imaging systems. Al-Rad Companion receives these data and checks which post-processing algorithms may be applicable. Data that does not meet the Al-Rad Companion requirements are ignored while data that meets the requirements are sent for further processing. Applicable data are processed, and the results are provided to the user via their clinical workplace. The user has the option to accept, review or withdraw single results of Al-Rad Companion.

Al-Rad Companion includes a software operating platform (Al-Rad Companion (Engine)) and optional clinical extensions such as Al-Rad Companion (Pulmonary), Al-Rad Companion (Musculoskeletal) and Al-Rad Companion (Cardiovascular). The clinical extensions are post-processing applications that operate on the Al-Rad Companion (Engine) software platform and process CT datasets in specific regions of the thorax or use datasets from other modalities. The basic post-processing functions are landmark detection, segmentation, and classification. Al-Rad Companion uses Artificial Intelligence (Al)algorithms.

The Al-Rad Companion (Engine) platform is the interface for incoming and outgoing data for the complete Al-Rad Companion system that provides input data and collects results and status information from the extensions. Additionally, it is the interface for incoming and outgoing data for the complete Al-Rad Companion system.

The Al-Rad Companion extensions are optional post-processing applications that operate on the Al-Rad Companion (Engine) software platform. The platform and each of the extensions are distinct software components and thus separate medical devices.

The scope of this submission is the extension Al-Rad Companion (Pulmonary). It is an image postprocessing software that uses CT DICOM data to support clinicians in the evaluation and assessment of lung diseases. It utilizes machine-learning and deep-learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the major functionalities of Al-Rad Companion (Pulmonary) are as follows:

• Segmentation and measurements of complete lung, lungs, and lung lobes.
• ldentification of areas with lower Hounsfield values in comparison to a predefined threshold for . complete lung and lung lobes.
• . Segmentation and measurements of found lung lesions
• . Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinguished

The results will be delivered in different image formats and, depending on the configuration, can be verified in the Results Preview and will be included in the overview with all findings. This will include DICOM Structured Report with measurements results

The software version VA13 of the Al-Rad Companion (Pulmonary) includes the following modifications:

• Pulmonary Density: This feature provides the possibility to segment opacity regions inside the lung using an Al algorithm. Al-Rad Companion (Pulmonary) counts image voxels inside opacity regions and calculates the percentages of these voxels relative to the total number of voxels per lobe, lung and in total. Afterwards, the opacity results are assigned to a certain range as defined by Bernheim et al.
• Bi-directional lesion diameter: This feature provides an additional measurement derived from the existing segmentation contour of a lung lesion. The existing list of measurements is extended with the maximum orthogonal diameter in 2D (short axis diameter) which is orthogonal to the lesion's maximum 2D diameter (2D diameter, long axis diameter).
• Cloud and Edge Deployment: The system supports the existing cloud deployment as well as a new edge deployment. The system remains hosted in the teamplay digital health platform and remains driven by the Al-Rad Companion (Engine). Now the edge deployment allows the processing of clinical data and the generation of results on-premises within the customer network. The edge system is fully connected to the cloud for monitoring and maintenance of the system from remote.

Here's a breakdown of the acceptance criteria and study details for Al-Rad Companion (Pulmonary) based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document mainly focuses on proving substantial equivalence to a predicate device, thus the "acceptance criteria" are implied to be achieving performance comparable to, or improving upon, the predicate. Specific numerical thresholds for acceptance criteria are not explicitly stated, but are inferred from the reported performance which is deemed acceptable for substantial equivalence.

2. Sample Size for the Test Set and Data Provenance

• Lung Lobe Segmentation: 250 datasets
• Opacity Detection: 150 datasets
• Data Provenance: Multiple sites across the US and Europe. The document states this was a "Clinical Data Based Software Validation." It does not explicitly state if it was retrospective or prospective, but clinical validation of existing images typically suggests a retrospective approach.

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

The document does not specify the number of experts or their qualifications for establishing ground truth for the test set. It mentions "human reads" for comparison in the opacity detection study, implying human experts were involved, but details are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1, none) used for the test set. For opacity detection, "Interreader-variability of the percentage of opacity (PO) was assessed" against "human reads," but the specific process of how those "human reads" were finalized as ground truth is not detailed.

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

No multi-reader multi-case (MRMC) comparative effectiveness study demonstrating human readers improve with AI vs. without AI assistance is explicitly described. The studies focus on the standalone performance of the AI algorithm against human reads or a "primary predicate device."

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance study was done. The "Clinical Evaluation of the AI-based Algorithms" section details the validation of the lung lobe segmentation and opacity detection algorithms. The reported DICE coefficients and the LoA for PO values are measures of the algorithm's performance independent of human-in-the-loop interaction in the context of the reported studies.

7. Type of Ground Truth Used

The ground truth used for the opacity detection algorithm was based on "human reads" during an inter-reader variability assessment. For lung lobe segmentation, while not explicitly stated, it is commonly established through expert annotations. The phrasing "Description of ground truth / annotations generation" indicates that such ground truth was generated, likely by experts.

8. Sample Size for the Training Set

The document mentions "Training cohort: size and properties of data used for training" under the "Clinical Data Based Software Validation" section but does not provide the specific sample size for the training set. It states "Additional training data was added as compared to the primary predicate for the Pulmonary Density Feature."

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

The document states "Description of ground truth / annotations generation" for the training cohort, implying that ground truth was established through annotations, most likely by clinical experts. However, specific details about the process (e.g., number of annotators, their qualifications, adjudication) are not provided for the training set.

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