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Chondral Quant is a musculoskeletal post-processing software application that allows assessment of knee cartilage condition based on Magnetic Resonance Imaging (MRI).

The medical device Chondral Quant, software version VA10A, is a musculoskeletal postprocessing application that allows evaluating the status of knee cartilage based on Magnetic Resonance Imaging (MRI).

The software is part of the syngo OpenApps framework and can be used from within syngo.via like any other syngo.via workflow.

Version VA10A is the initial version of this medical device.

Chondral Quant processes a morphological 3D series of the knee joint and performs an automated segmentation of the knee's cartilage. The segmentation may be modified by the user. Chondral Quant will also perform a sub-segmentation of the knee cartilage. Chondral Quant additionally performs volumetry and thickness calculation on the segmented areas. Optionally, it is possible to provide a parametric map as secondary input to Chondral Quant. Commonly, this will be a T2 or T2* map. Chondral Quant will perform a registration of the morphological image and the parametric map and transfer the segmentation to the parametric map.

A statistical evaluation of the mapping results for the 21 sub zones will be additionally provided in this case.

All output will be provided in the form of a table showing statistical evaluation of the assessment (volume and mean, median and standard deviation of thickness and mapping results for every region). Additionally, Chondral Quant will generate output maps (segmentation map, cartilage thickness map) and 3D models of the segmented cartilage (zone model and thickness model). Finally, a PDF report containing the table values and the 3D models will be generated as output.

Alternatively, the application Chondral Quant may also be executed in a "PACS-ready" mode, i.e. fully-automated and without user interaction. The results will be sent to PACS automatically. In this case the series names will be marked with a prefix "AUTO GENERATED" for a clear indication of the automatic mode.

The subject device, Chondral Quant with software VA10A, consists of new and modified features that are similar to what is currently offered on the predicate device. The subject device includes the following modifications in comparison to the predicate device: new body region compared to predicate device:

• -Automatic segmentation and volumetry of the cartilage
• -Thickness calculation

The provided text does not contain detailed acceptance criteria and a study demonstrating how the device met those criteria in a format that allows for a direct population of the requested table. The document primarily focuses on explaining the device's intended use, its features, and justifying its substantial equivalence to a predicate device. It mentions "Nonclinical Tests" and "Solution Validation Summary Report" but does not elaborate on specific performance metrics, acceptance thresholds, or detailed study designs for those tests.

However, based on the available information, I can infer some aspects and highlight what is missing:

Here's an attempt to fill out the information based on the provided text, with explicit notes on what is not available:

1. Table of acceptance criteria and reported device performance:

The document mentions "Verification and validation... have been performed" and "All risk mitigations... and all relevant SSRS/FS requirements... have been tested and verified successfully." However, it does not provide a specific table of acceptance criteria (e.g., minimum accuracy, sensitivity, specificity for cartilage segmentation or volume/thickness calculation) nor does it report the device's performance against such criteria with quantitative measures.

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

• Test set sample size: "more than 100 cases" for 3T MRI systems, "more than 11 cases" for 7T MRI systems, and "more than 9 cases" for 1.5T MRI systems.
• Data provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective.

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

• Number of experts: Not specified.
• Qualifications of experts: Not specified. The document does not mention the involvement of experts for establishing ground truth for the test set.

4. Adjudication method for the test set:

• Adjudication method: Not specified. There is no mention of how ground truth was established, let alone an adjudication method.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size:

• MRMC study: No. The document explicitly states: "No clinical tests were conducted for the subject device." Therefore, no MRMC study was performed, and no effect size for human readers improving with AI assistance is reported.

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

• Standalone study: The document implies standalone performance testing was done for the algorithm's functionality and accuracy in segmentation, volumetry, and thickness calculation during non-clinical validation. The text states: "Chondral Quant processes a morphological 3D series of the knee joint and performs an automated segmentation of the knee's cartilage. The segmentation may be modified by the user. Chondral Quant will also perform a sub-segmentation of the knee cartilage. Chondral Quant additionally performs volumetry and thickness calculation on the segmented areas." It also mentions an optional "PACS-ready" mode which is "fully-automated and without user interaction," suggesting a standalone capability. However, specific performance metrics from a standalone study are not provided.

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

• Type of ground truth: Not explicitly stated. Given the nature of cartilage segmentation, volumetry, and thickness calculation, it is highly probable that expert-annotated segmentations or measurements (potentially from radiologists or musculoskeletal specialists) were used as ground truth for comparison. However, the document does not confirm this or specify the methodology for ground truth creation.

8. The sample size for the training set:

• Training set sample size: "more than 31" cases.

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

• Ground truth for training set: Not specified. The document does not detail how the ground truth for the training data (e.g., labeled segmentations of cartilage) was established.

Summary of Missing Information:

The provided FDA 510(k) summary focuses on demonstrating substantial equivalence rather than providing a detailed performance study report with specific acceptance criteria and quantitative results. Key missing information includes:

• Specific quantitative acceptance criteria for segmentation, volumetry, and thickness.
• Quantitative results of the device's performance against these criteria.
• Details on the data provenance (country, retrospective/prospective nature) for test data.
• The number and qualifications of experts involved in establishing ground truth for any test or training sets.
• The methodology for establishing ground truth (e.g., expert consensus, manual annotation rules).
• Any details of reader studies (MRMC or otherwise) due to the statement that no clinical tests were conducted.

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syngo.CT Cardiac Planning is an image analysis software package for evaluating contrast enhanced CT images. The software package is designed to support the physician in the qualitative and quantitative analysis of morphology and pathology of vascular and cardiac structures, with the overarching purpose of serving as input for planning of cardiovascular procedures.

syngo.CT Cardiac Planning is an image analysis software package for evaluating contrast enhanced CT images. The software package is designed to support the physician in the qualitative and quantitative analysis of morphology and pathology of vascular and cardiac structures, with the overarching purpose of serving as input for planning of cardiovascular procedures.

syngo.CT Cardiac Planning includes tools that support the clinician at different steps during diagnosis, including reading and reporting. The user has full control of the reported measurements and images and is able to choose the appropriate function suited for their clinical need. Features included in this software that aid in diagnosis can be grouped in the following categories:

• . Basic reading: commodity features that are commonly available on CT cardiac postprocessing workstations
• Advanced reading: additional features for increased user support during CT cardiac ● postprocessing.

This document, K200515, describes Siemens' syngo.CT Cardiac Planning software. It states that the submission aims to clear an "error correction that return the Cardiac Planning software to its original specifications" and mentions that there are "no differences between the subject device and the predicate device" and no "new features or modification to already cleared features." Based on this, a full comparative effectiveness study with human readers (MRMC) or a standalone (algorithm only) performance study against clinical ground truth is not expected or provided. The document focuses on verification and validation demonstrating that the software performs as intended after the error correction, aligning with the original cleared specifications.

Given the nature of this 510(k) submission, the provided text does not contain information about specific acceptance criteria related to clinical performance metrics (like sensitivity, specificity, accuracy) or a study proving the device meets these criteria in the typical sense for a new AI/ML device. Instead, the "acceptance criteria" discussed are largely related to software verification and validation, ensuring the corrected software meets its design specifications and maintains substantial equivalence to the predicate device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of performance acceptance criteria (e.g., sensitivity, specificity, or specific measurement accuracy thresholds) for the device's diagnostic capabilities. Instead, it refers to:

The "Correction of the measurement algorithm" for "Measurement Tools" within the TAVI Feature is the specific area where a change was made and subsequently verified. The performance reported is that this correction brings the software back to its "original specifications" and achieves "substantially equivalent" performance to the predicate.

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

The document does not specify the sample size of a test set (e.g., number of patient cases) used for clinical performance evaluation. The testing described is primarily focused on software verification and validation, rather than a clinical performance study with patient data. Therefore, data provenance (country of origin, retrospective/prospective) is not applicable in the context of clinical performance evaluation.

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

Not applicable, as a clinical performance study with a test set requiring expert ground truth is not detailed in this submission. The focus is on software function and correction verification.

4. Adjudication Method for the Test Set:

Not applicable for the same reasons as above.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted and is not mentioned. The submission's core purpose is to demonstrate that an error correction maintains the device's original cleared specifications, not to show improvement over human readers.

6. Standalone (Algorithm Only) Performance Study:

A standalone (algorithm only) performance study (e.g., measuring diagnostic accuracy independent of a human user) was not conducted and is not described. The document pertains to an error correction in existing, cleared software.

7. Type of Ground Truth Used:

The document implies a ground truth based on the original design specifications and expected behavior of the syngo.CT Cardiac Planning software (K170221). The testing confirmed that the corrected measurement algorithm performs according to these original specifications, which serve as the implicit "ground truth" for the verification activities. There is no mention of external clinical ground truth (e.g., pathology, outcomes data) for validating a diagnostic claim in this submission.

8. Sample Size for the Training Set:

Not applicable. This submission is for an error correction to an existing software product, not the development of a new algorithm that would involve a training set.

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

Not applicable, as no training set is mentioned or implied for this submission.

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syngo.via MI Workflows are medical diagnostic applications for viewing, manipulation, 3D- visualization and comparison of medical images from multiple imaging modalities and/or multiple time-points. The apports functional data, such as PET or SPECT as well as anatomical datasets, such as CT or MR.

syngo.via MI Workflows enable visualization of information that would otherwise have to be visually compared disjointedly. syngo.via MI Workflows provide analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations. syngo via MI Workflows can perform harmonization of SUV (PET) across different PET systems or different reconstruction methods.

syngo.via MI workflows support the interpretation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology environments.

Note: The clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices and visual comparison of the separate unregistered images. syngo.via MI Workflows are a complement to these standard procedures.

The Scenium display and analysis software has been developed to aid the Clinician in the assessment and quantification of pathologies taken from PET and SPECT scans.

The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with particular drug and disease combinations.

The software aids in the assessment of human brain scans enabling automated analysis through quantification of mean pixel values located within standard regions of interest. It facilitates comparison with existing scans derived from FDGPET, amyloid-PET, and SPECT studies, calculation of uptake ratios between regions of interest, and subtraction between two functional scans.

syngo.via MI Workflows is a software-only medical device which will be delivered on CD-ROM / DVD to be installed onto the commercially available Siemens syngo.via software platform (K191040) by trained service personnel.

syngo.via MI Workflows is a medical diagnostic application for viewing, manipulation, 3Dvisualization and comparison of medical images from multiple imaqinq modalities and/or multiple time-points. The application supports functional data, such as PET or SPECT as well as anatomical datasets, such as CT or MR. The images can be viewed in a number of output formats including MIP and volume rendering.

syngo.via MI Workflows enable visualization of information that would otherwise have to be visually compared disjointedly. syngo.via MI Workflows provide analytical tools to help the user assess, and document changes in morphological or functional activity at diagnostic and therapy follow-up examinations. They additionally support the interpretation and evaluation of examinations and follow up documentation of findings within healthcare institutions, for example, in Radiology (Oncology), Nuclear Medicine and Cardiology environments.

Scenium display and analysis software sits within the MI Neurology workflow within syngo.via MI Workflows. This software enables visualization and appropriate rendering of multimodality data, providing a number of features which enable the user to process acquired image data.

Scenium consists of four workflows:

• -Database Comparison
• -Striatal Analysis
• Cortical Analysis -
• -Subtraction

These workflows are used to assist the clinician with the visual evaluation, assessment and quantification of pathologies, such as dementia (i.e., Alzheimer's), movement disorders (i.e., Parkinson's) and seizure analysis (i.e., Epilepsy).

The modifications to the syngo.via MI Workflows and Scenium (MI Neurology) software (K173897 and K173597) include the following new features:

The provided text does not contain detailed information about specific acceptance criteria or a dedicated study proving the device meets those criteria. The document is a 510(k) summary for the syngo.via MI Workflows VB40A, Scenium device, primarily focusing on demonstrating substantial equivalence to a predicate device.

However, it does mention that "Verification and Validation activities have been successfully performed on the software package, including assurance that functions work as designed, performance requirements and specifications have been met, and that all hazard mitigations have been fully implemented. All testing has met the predetermined acceptance values." This generally indicates that internal testing was conducted against defined acceptance criteria, but these criteria and the detailed results are not explicitly stated.

Therefore, many of the requested details cannot be extracted from the provided text.

Here's what can be gathered, with limitations:

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

• Not explicitly provided. The document states "All testing has met the predetermined acceptance values," but does not list specific acceptance criteria or the quantitative performance metrics achieved.

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

• Not provided. The document does not discuss the sample size or provenance of data used for any performance testing.

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)

• Not provided. This information is absent from the text.

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

• Not provided. The document does not describe any adjudication methods.

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

• Not provided. The document does not mention any MRMC comparative effectiveness studies or the impact of the device on human reader performance. The device is described as a diagnostic application for viewing, manipulation, 3D-visualization, and comparison, and its role is to "complement these standard procedures," but no specific reader studies are detailed.

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

• Not explicitly stated as a standalone study in the context of clinical performance. The device itself is software-only, meaning its "standalone" functionality is its core operation. However, the document does not present specific performance metrics that would be associated with a standalone algorithm performance study (e.g., sensitivity, specificity for a particular pathology detection). It focuses on the software's ability to view, manipulate, and analyze images, and that "All testing has met the predetermined acceptance values," implying functional and performance testing, but not necessarily a clinical standalone performance study in the way AI algorithms are often evaluated for diagnostic accuracy.

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

• Not provided. The document does not specify how ground truth was established for any testing.

8. The sample size for the training set

• Not provided. The document does not discuss any training sets, suggesting that this device might not involve a machine learning model that requires a distinct training phase in the traditional sense, or at least that information is not part of this 510(k) summary. Given the device's description as an "Image Processing Software" that provides "analytical tools," it's more likely rule-based or using established algorithms rather than a deep learning model requiring extensive training data.

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

• Not applicable / Not provided. As no training set information is given, this question cannot be answered.

In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence to predicate devices and adherence to quality systems and standards (ISO 14971, IEC 62304), rather than detailing specific clinical performance studies with acceptance criteria, ground truth, or reader study results. The statement about "All testing has met the predetermined acceptance values" is a general confirmation of internal verification and validation.

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