Search Results

Syngo Carbon Clinicals is intended to provide advanced visualization tools to prepare and process the medical image for evaluation, manipulation and communication of clinical data that was acquired by the medical imaging modalities (for example, CT, MR, etc.)

The software package is designed to support technicians and physicians in qualitative and quantitative measurements and in the analysis of clinical data that was acquired by medical imaging modalities.

An interface shall enable the connection between the Syngo Carbon Clinicals software package and the interconnected software solution for viewing, manipulation, communication, and storage of medical images.

Syngo Carbon Clinicals is a software only Medical Device, which provides dedicated advanced imaging tools for diagnostic reading. These tools can be called up using standard interfaces any native/syngo based viewing applications (hosting applications) that is part of the SYNGO medical device portfolio. These tools help prepare and process the medical image for evaluation, manipulation and communication of clinical data that was acquired by medical imaging modalities (e.g., MR, CT etc.)

Deployment Scenario: Syngo Carbon Clinicals is a plug-in that can be added to any SYNGO based hosting applications (for example: Syngo Carbon Space, syngo.via etc...). The hosting application (native/syngo Platform-based software) is not described within this 510k submission. The hosting device decides which tools are used from Syngo Carbon Clinicals. The hosting device does not need to host all tools from the Syngo Carbon Clinicals, a desired subset of the provided tools can be used. The same can be enabled or disabled thru licenses.

The provided text is a 510(k) summary for Syngo Carbon Clinicals (K232856). It focuses on demonstrating substantial equivalence to a predicate device through comparison of technological characteristics and non-clinical performance testing. The document does not describe acceptance criteria for specific device performance metrics or a study that definitively proves the device meets those criteria through clinical trials or quantitative bench testing with specific reported performance values.

Instead, it relies heavily on evaluating the fit-for-use of algorithms (Lesion Quantification and Organ Segmentation) that were previously studied and cleared as part of predicate or reference devices, and ensuring their integration into the new device without modification to the core algorithms. The non-clinical performance testing for Syngo Carbon Clinicals focuses on verification and validation of changes/integrations, and conformance to relevant standards.

Therefore, many of the requested details about acceptance criteria and reported device performance cannot be extracted directly from this document. However, I can provide information based on what is available.

Acceptance Criteria and Study for Syngo Carbon Clinicals

Based on the provided 510(k) summary, formal acceptance criteria with specific reported performance metrics for the Syngo Carbon Clinicals device itself are not explicitly detailed in a comparative table against a clinical study's results. The submission primarily relies on the equivalency of its components to previously cleared devices and non-clinical verification and validation.

The "study" proving the device meets acceptance criteria is fundamentally a non-clinical performance testing, verification, and validation process, along with an evaluation of previously cleared algorithms from predicate/reference devices for "fit for use" in the subject device.

Here's a breakdown of the requested information based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned, a direct table of specific numerical acceptance criteria and a corresponding reported device performance from a clinical study is not present. The document describes acceptance in terms of:

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

• Sample Size for Test Set: Not explicitly stated for specific test sets in this document for Syngo Carbon Clinicals. The evaluations of the Lesion Quantification and Organ Segmentation algorithms refer to "phantom and reader studies" from their respective predicate/reference devices, but details on the sample sizes of those original studies are not provided here.
• Data Provenance: Not specified. The original "phantom and reader studies" for the algorithms were likely internal to the manufacturers or collaborators, but this document does not detail their origin (e.g., country, specific institutions). The text indicates these were retrospective studies (referring to prior evaluations).

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

• Number of Experts: Not specified. The document mentions "reader studies" were conducted for the predicate/reference devices' algorithms, implying involvement of human readers/experts, but the number is not stated.
• Qualifications of Experts: Not specified. It can be inferred that these would be "trained medical professionals" as per the intended user for the device, but specific qualifications (e.g., radiologist with X years of experience) are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified for the historical "reader studies" referenced. This document does not detail the methodology for establishing ground truth or resolving discrepancies among readers if multiple readers were involved.

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

• MRMC Comparative Effectiveness Study: The document itself 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." Therefore, no MRMC comparative effectiveness study for human readers with and without AI assistance for Syngo Carbon Clinicals was performed or reported in this submission. The device is a set of advanced visualization tools, not an AI-assisted diagnostic aid that directly impacts reader performance in a comparative study mentioned here.

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

• Standalone Performance: The core algorithms (Lesion Quantification and Organ Segmentation) were evaluated in "phantom and reader studies" as part of their previous clearances (predicate/reference devices). While specific standalone numerical performance metrics for these algorithms (e.g., sensitivity, specificity, accuracy) are not reported in this document, the mention of "phantom" studies suggests a standalone evaluation component. The current submission, however, evaluates these previously cleared algorithms for "fit for use" within the new Syngo Carbon Clinicals device, implying their standalone performance was considered acceptable from their original clearances.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Not explicitly detailed. The referenced "phantom and reader studies" imply that for phantoms, the ground truth would be known (e.g., physical measurements), and for reader studies, it would likely involve expert consensus or established clinical benchmarks. However, the exact method for establishing ground truth in those original studies is not provided here.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not specified in this 510(k) summary. The document mentions that the deep learning algorithm for organ segmentation was "cleared as part of the reference device syngo.via RT Image suite (K220783)." This implies that any training data for this algorithm would have been part of the K220783 submission, not detailed here for Syngo Carbon Clinicals.

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

• Ground Truth for Training Set: Not specified in this 510(k) summary. As with the training set size, this information would have been part of the original K220783 submission for the organ segmentation algorithm and is not detailed in this document.

Ask a specific question about this device