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syngo Virtual Cockpit is a software application intended for remote operation, assistance, review, monitoring, and standardization of medical imaging devices. It is a vendor neutral solution allowing read-only or full access control to connected devices. syngo Virtual Cockpit is also intended for training of medical personnel working on the medical imaging devices.

syngo Virtual Cockpit (sVC) is a software solution for geographically distant technologist or radiologists to remotely assist with operating imaging equipment and radiotherapeutic devices. sVC provides a private, secure communication platform for real-time image visualization and crossorganizational collaboration between healthcare professionals across multiple sites. sVC enables remote access to modality consoles and enhances communication capabilities between healthcare professionals across different locations. It is vendor-neutral and applicable to existing multimodalities in a healthcare network, a solution that allows healthcare professionals to share expertise and increase productivity, even when they are not physically present in the same location. sVC is based on a client server architecture, with sVC server as the backbone and 3 different variants of client, based on the user roles, Modality client & Physician client. Modality client as two flavors one windows based client and a web client which can be hosted in a web browser. Steering client establishes remote connection to Modality console / Modality acquisition workplace through KVM (Keyboard, Video and Mouse) switch or Siemens proprietary accessing tool, syngo Expert-i. Steering client can establish connections to more than one (up to 3) Modality console applications. Physician client is the third client for Physician that can be contacted either by Steering technologist or by Modality technologist for assistance regarding scanning in more complex cases, or the Physician can provide expert radiologist knowledge. The connection is possible in full control or read-only mode. The full-control accessibility to CT scanners is limited to the software associated with the modality workplace and is not applicable to the physical switches controlling the equipment operation. The connection to radiotherapeutic equipment is limited to be read-only. In addition to enabling remote access and control of the modality scanners, sVC also supports common communication methods including live videos at the modality site, audio calls and text chats among users.

The provided FDA 510(k) summary for syngo Virtual Cockpit (VB10A) focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study for novel performance claims. As such, it does not contain the typical elements of an acceptance criteria table or a comparative effectiveness study (such as MRMC) that would be expected for a diagnostic AI device requiring a clinical performance evaluation.

The device, "syngo Virtual Cockpit (VB10A)," is categorized as a "Medical Image Management And Processing System" (MIMPS) with product code LLZ and is a software-only solution intended for remote operation, assistance, review, monitoring, and standardization of medical imaging devices, and for training medical personnel. It is explicitly stated that "Images reviewed remotely are not for diagnostic use." This indicates it is not a diagnostic AI device that would produce specific diagnostic outputs requiring performance metrics like sensitivity, specificity, or AUC against a ground truth.

Therefore, the information typically requested in your prompt (e.g., sample size for test/training sets, number of experts for ground truth, adjudication methods, MRMC study effect sizes, standalone performance, type of ground truth) is not present in this document because the device's function does not necessitate such a clinical performance evaluation.

Here's an analysis based on the information available in the document:

Acceptance Criteria and Device Performance (as inferred from the document's V&V approach):

Instead of clinical performance metrics, the acceptance criteria are focused on the functional validity, safety, and effectiveness of the remote management system compared to its predicate and the general requirements for medical device software.

Details Regarding the Study (as per provided text):

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

   • Test Set Sample Size: Not explicitly stated as a number of "cases" or "patients" in the traditional sense of a clinical study for diagnostic performance. The testing involved "externally sourced representative modality scanners" and "the entire system."
   • Data Provenance: The testing was conducted "in the Siemens Healthineers training center and at the clinical collaborating site." This suggests controlled, simulated environments and potentially real clinical settings, but the origin of any "data" would be the performance of the system itself during validation, not pre-existing patient data. It was effectively a prospective functional validation.

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

   • Not Applicable in the traditional sense. There is no "ground truth" established by experts for diagnostic purposes because the device does not make diagnostic claims or generate diagnostic outputs. The "ground truth" for this device would be its ability to correctly perform its intended functions (remote control, video, audio, chat, latency, etc.) and comply with safety and cybersecurity standards. The "evaluation by clinicians" refers to their use of the system and their ability to detect malfunctions during its operation, not a judgmental "ground truth" on diagnoses.

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

   • Not Applicable. Adjudication methods are typically used to resolve discrepancies in expert readings or interpretations when establishing ground truth for diagnostic studies. This device's validation focused on functional performance and compliance.

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, an MRMC comparative effectiveness study was explicitly NOT done. The document states: "No clinical studies were carried out for syngo Virtual Cockpit (Version VB10A). All performance testing was conducted in a non-clinical fashion as part of the verification and validation activities for the medical device." This device is not designed to assist with human reading of images but rather with operating and managing the imaging process remotely.

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

   • Not Applicable. The device, by its nature, is a human-in-the-loop system for remote operation. Its functionality is the human-in-the-loop performance (e.g., remote control inputs, video/audio communication). "Stand-alone" performance would refer to diagnostic output generation without human intervention, which is not what this device does.

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

   • Not Applicable in the traditional sense for diagnostic performance. The "ground truth" relevant to this device's validation is the successful execution of its defined functional requirements and user stories, compliance with design inputs, and adherence to relevant safety and regulatory standards (e.g., IEC 62366-1, ISO 14971, IEC 62304). This is assessed through structured verification and validation testing rather than comparison to a clinical gold standard for disease.

7. The sample size for the training set:

   • Not Applicable. This device is a software application for remote operation and management, not a machine learning or AI algorithm that trains on a dataset to learn patterns or make predictions. Therefore, there is no "training set" in the context of an AI model.

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

   • Not Applicable. As there is no training set for an AI model, there is no ground truth associated with it.

In summary: The K232744 submission for syngo Virtual Cockpit (VB10A) is a 510(k) for a Medical Image Management and Processing System, not a diagnostic AI device. Its validation focuses on functional performance, safety, cybersecurity, and substantial equivalence to a predicate device based on those characteristics, rather than clinical performance metrics like accuracy, sensitivity, or specificity. Therefore, many of the questions regarding clinical study design, ground truth, and AI model training are not relevant to this specific device submission.

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