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syngo.MR Applications is a syngo based post-acquisition image processing software for viewing, manipulating, evaluating, and analyzing MR, MR-PET, CT, PET, CT-PET images and MR spectra.

Device Description

syngo.MR Applications is a software only Medical Device consisting post-processing applications/workflows used for viewing and evaluating the designated images provided by a MR diagnostic device. The post-processing applications/workflows are integrated with the hosting application syngo.via, that enables structured evaluation of the corresponding images

AI/ML Overview

The provided FDA 510(k) clearance letter and summary for syngo.MR Applications (VB80) indicate that no clinical studies or bench testing were performed to establish new performance criteria or demonstrate meeting previously established acceptance criteria. The submission focuses on software changes and enhancements from a predicate device (syngo.MR Applications VB40).

Therefore, based solely on the provided document, I cannot create the requested tables and information because the document explicitly states:

"No clinical studies were carried out for the product, all performance testing was conducted in a non-clinical fashion as part of verification and validation activities of the medical device."
"No bench testing was required to be carried out for the product."

The document details the following regarding performance and acceptance:

Non-clinical Performance Testing: "Non-clinical tests were conducted for the subject device during product development. The modifications described in this Premarket Notification were supported with verification and validation testing."
Software Verification and Validation: "The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests were conducted on the device Syngo.MR Applications during product development... The testing results support that all the software specifications have met the acceptance criteria. Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence."
Conclusion: "The predicate device was cleared based on non-clinical supportive information. The comparison of technological characteristics, device hazards, non-clinical performance data, and software validation data demonstrates that the subject device performs comparably to and is as safe and effective as the predicate device that is currently marketed for the same intended use."

This implies that the acceptance criteria are related to the functional specifications and performance of the software, as demonstrated by internal verification and validation activities, rather than a clinical performance study with specific quantitative metrics. The new component, "MR Prostate AI," is noted to be integrated without modification and had its own prior 510(k) clearance (K241770), suggesting its performance was established in that separate submission.

Without access to the actual verification and validation reports mentioned in the document, it's impossible to list precise acceptance criteria or detailed study results. The provided text only states that "all the software specifications have met the acceptance criteria."

Therefore, I can only provide an explanation of why the requested details cannot be extracted from this document:

Explanation Regarding Acceptance Criteria and Study Data:

The provided FDA 510(k) clearance letter and summary for syngo.MR Applications (VB80) explicitly state that no clinical studies or bench testing were performed for this submission. The device (syngo.MR Applications VB80) is presented as a new version of a predicate device (syngo.MR Applications VB40) with added features and enhancements, notably the integration of an existing AI algorithm, "Prostate MR AI VA10A (K241770)," which was cleared under a separate 510(k).

The basis for clearance is "non-clinical performance data" and "software validation data" demonstrating that the subject device performs comparably to and is as safe and effective as the predicate device. The document mentions that "all the software specifications have met the acceptance criteria" as part of the verification and validation (V&V) activities. However, the specific quantitative acceptance criteria, detailed performance metrics, sample sizes, ground truth establishment, or expert involvement for these V&V activities are not included in this public summary.

Therefore, the requested information cannot be precisely extracted from the provided text.

Summary of Information Available (and Not Available) from the Document:

Information Requested	Status (Based on provided document)
1. Table of acceptance criteria and reported performance	Not provided in the document. The document states: "The testing results support that all the software specifications have met the acceptance criteria." However, it does not specify what those acceptance criteria are or report detailed performance metrics against them. These would typically be found in the detailed V&V reports, which are not part of this summary.
2. Sample size and data provenance for test set	Not provided. The document indicates "non-clinical tests were conducted as part of verification and validation activities." The sample sizes for these internal tests, the nature of the data, and its provenance (e.g., country, retrospective/prospective) are not detailed. It is implied that the data is not patient-specific clinical test data.
3. Number of experts and qualifications for ground truth	Not applicable/Not provided. Since no clinical studies or specific performance evaluations against an external ground truth are described in this document, there's no mention of experts establishing ground truth for a test set. The validation appears to be against software specifications. If the "MR Prostate AI" component had such a study, those details would be in its individual 510(k) (K241770), not this submission.
4. Adjudication method for test set	Not applicable/Not provided. As with the ground truth establishment, no adjudication method is mentioned because no external test set requiring such expert consensus is described within this 510(k) summary.
5. MRMC comparative effectiveness study and effect size	Not performed for this submission. The document explicitly states "No clinical studies were carried out for the product." Therefore, no MRMC study or AI-assisted improvement effect size is reported here.
6. Standalone (algorithm only) performance study	Partially addressed for a component. While this submission doesn't detail such a study, it notes that the "MR Prostate AI" algorithm is integrated without modification and "is classified under a different regulation in its 510(K) and this is out-of-scope from the current submission." This implies that a standalone performance study was done for the Prostate MR AI algorithm under its own 510(k) (K241770), but those details are not within this document. For the overall syngo.MR Applications (VB80) product, no standalone study is described.
7. Type of ground truth used	Not provided for the overall device's V&V. The V&V activities are stated to have met "software specifications," which suggests an internal, design-based ground truth rather than clinical ground truth like pathology or outcomes data. For the integrated "MR Prostate AI" algorithm, clinical ground truth would have been established for its separate 510(k) submission.
8. Sample size for the training set	Not applicable/Not provided for this submission. The document describes internal non-clinical V&V for the syngo.MR Applications software. It does not refer to a machine learning model's training set within this context. The "Prostate MR AI" algorithm, being independently cleared, would have its training set details in its specific 510(k) dossier (K241770), not here.
9. How the ground truth for the training set was established	Not applicable/Not provided for this submission. As above, this document does not discuss a training set or its ground truth establishment for syngo.MR Applications. This information would pertain to the Prostate MR AI algorithm and be found in its own 510(k).

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