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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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Digital Expert Access with Remote Scanning is intended as a remote collaboration tool to view and review MR images, to remotely control MR Imaging Devices and to initiate MRI scans remotely.

Digital Expert Access with Remote Scanning is a remote scan assistance solution which allows remote control of an MR Imaging Device including the ability to initiate a scan remotely. This access provides real time communication mechanisms between the remote and onsite users to facilitate the acquisition occurring on the device. Access must be granted by the onsite user operating the system. Images reviewed remotely are not for diagnostic use.

Digital Expert Access is a variation of the Customer Remote Console cleared under K150193. It is a remote scan assistance solution designed to address the skill variability in technologists and their need for ondemand support by allowing them to interact directly with a remote expert connected to the hospital network. By enabling the collaboration between an Onsite Technologist and Remote Expert, Digital Expert Access helps the onsite technologist to seek guidance and real time support on scanning related queries including but not limited to training, procedure assessment, and scanning parameter management.

Digital Expert Access with Remote Scanning introduces a feature that enables the Remote Expert to initiate a scan and make changes in real time during the scanning session. This remote scan feature is only available when Digital Expert Access is connected to a compatible GE HealthCare MRI system.

Digital Expert Access with Remote Scanning enables the following capabilities for the Onsite Technologist and the Remote Expert:

• 1. Collaborative session between an Onsite Technologist and Remote Expert
• 2. Real-time scanner screen share and live annotation
• ന് Remote console access and control
• 4. Remote Scan Initiation

Digital Expert Access with Remote Scanning is not intended for diagnostic use or patient safety-related management. This solution is not intended to be used by individuals who are not properly trained in the operation of GE HealthCare Medical Imaging systems. Digital Expert Access with Remote Scanning does not directly interface with any patients and requires the Onsite Technologist to be continuously present throughout the scanning procedure. Digital Expert Access with Remote Scanning does not acquire any MRI images, nor does it do any post image processing. All image acquisition and image processing is conducted by the GE HealthCare MRI system.

The provided text describes a 510(k) premarket notification for a device called "Digital Expert Access with Remote Scanning." This document outlines the device's purpose, intended use, and substantial equivalence to predicate devices, but it does not contain information about specific acceptance criteria or an analytical study proving the device meets those criteria in terms of performance metrics like accuracy, sensitivity, or specificity.

The document emphasizes that the device did not require clinical studies to support substantial equivalence. Instead, the focus was on non-clinical tests, primarily design verification and validation testing, to ensure proper implementation of design requirements and that no new safety or effectiveness issues were introduced.

Therefore, many of the requested details about acceptance criteria for performance, sample sizes, ground truth establishment, expert involvement, and MRMC studies are not present in this document because the submission strategy relied on substantial equivalence based on technological similarity and non-clinical testing rather than performance studies against defined quantitative acceptance criteria.

However, based on the information provided, here's what can be extracted and inferred regarding the "acceptance criteria" and "study":

Acceptance Criteria (Inferred from Non-Clinical Testing and Device Functionality):

Since no performance metrics are given, the acceptance criteria are implicitly related to the successful functionality and safety of the remote scanning feature. These would likely be qualitative or functional:

Study Information (Based on Non-Clinical Testing):

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

   • Sample Size: Not explicitly stated as a number of "cases" or "patients" in a clinical study. The testing was conducted on a "subset of GE HealthCare MRI systems" on the bench. This implies a sample of hardware configurations rather than patient data.
   • Data Provenance: Not applicable as it was non-clinical "bench" testing, not involving patient data or a specific country of origin for such data. It was performed internally by GE HealthCare.

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

   • Not applicable. As this was non-clinical, functional testing, there was no "ground truth" in the clinical diagnostic sense established by experts. Testing would have involved engineers and quality assurance personnel verifying the system's intended functionality.

3. Adjudication Method for the Test Set:

   • Not applicable for functional verification testing. Adjudication methods are typically used when comparing human interpretations or algorithmic outputs against a gold standard in diagnostic or clinical performance studies.

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 not done. The document explicitly states: "The subject of this premarket submission, Digital Expert Access with Remote Scanning did not require clinical studies to support substantial equivalence..." The device is a remote control and collaboration tool, not an AI-assisted diagnostic device meant to improve human reader performance.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

   • Not applicable in the context of performance. The device is inherently a "human-in-the-loop" collaboration tool. While its technical components were likely tested in isolation (standalone components), the overall device's "performance" is tied to its functional remote control and communication capabilities, not an algorithmic diagnostic output.

6. The Type of Ground Truth Used:

   • For the non-clinical testing, the "ground truth" was the pre-defined design requirements and expected functional behavior of the device. This is typical for engineering verification and validation testing, where the "truth" is whether the system performs as designed and specified (e.g., does the remote expert successfully initiate a scan?).

7. The Sample Size for the Training Set:

   • Not applicable. The document does not describe the device as employing machine learning or AI that would require a "training set" of data in the sense of a deep learning model. It is a control and communication software system.

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

   • Not applicable. As no training set for a machine learning model is mentioned, ground truth establishment for such a set is irrelevant to this submission.

In summary, the FDA 510(k) clearance for this device was based on a demonstration of "substantial equivalence" to existing predicate devices through non-clinical design verification and validation testing, focusing on ensuring the new remote scanning feature did not introduce new safety or effectiveness issues, rather than requiring quantitative performance studies with clinical acceptance criteria.

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The syngo MultiModality Workplace is a medical diagnostic workstation for viewing, manipulation, communication, and storage of medical images and data on exchange media.

The syngo MultiModality Workplace can be configured as a stand-alone diagnostic review and post-processing workplace with a variety of syngo- or Windows XP-based software options, that are intended to assist the physician in diagnosis, surgical planning, interventional procedures or treatment planning. These options include commercially available post-processing software.

The syngo MultiModality Workplace does not support the display of mammography images for diagnosis.

This premarket notification covers Siemens syngo MultiModality Workplace (syngo MM WP), a syngo-based workplace that supports different modalities. syngo is a universal imaging platform based on Windows XP. syrgo MultiModality Workplace offers a comprehensive solution to view, optimize, process diagnostic information and aid the doctors in the evaluation of digital radiological examinations and patient information.

Due to special customer requirements based on the modality image type and the clinical focus, the syngo MultiModality Workplace can be configured with different combinations of clinical applications. syngo applications can be added to the multimodality workplace either individually or as clinically focused packages.

The syneo MultiModality Workplace is a medical diagnostic workplace for realtime viewing, manipulation, communication, and storage of medical images and data on exchange media.

The syrgo MultiModality Workplace can be configured as a stand-alone diagnostic review and post-processing workplace with a variety of syngo- or Windows XPbased software options, that are intended to assist the physician in diagnosis, surgical planning, interventional procedures or treatment planning. These options include commercially available post-processing software. The syngo MultiModality Workplace does not support the display of mammography images for diagnosis.

The syngo MultiModality Workplace will be marketed as a software only solution for the end-user (with recommended hardware requirements) or as a complete work station for the end-user (hardware and software package). It will be installed by Siemens service engineers. The syngo MultiModality Workplace described supports DICOM formatted images and information. The workplace is based on the Windows XP operating system.

This 510(k) submission for the Siemens syngo MultiModality Workplace (syngo MMWP) does not include a study proving device performance against acceptance criteria in the typical sense of a clinical or performance study with metrics like sensitivity, specificity, or reader agreement.

Instead, this submission is centered on demonstrating substantial equivalence to a previously cleared predicate device (syngo MultiModality Workplac (K052775) and MAGNETOM Systems with Expert-i option (K052423)). For general PACS (Picture Archiving and Communications System) software like the syngo MMWP, the "acceptance criteria" are primarily related to conforming to established standards, ensuring proper image display and manipulation, and maintaining data integrity, rather than diagnostic performance metrics.

Therefore, many of the requested points below cannot be directly extracted from the provided text, as the focus is on safety, effectiveness, and equivalence to existing technology.

Here's an breakdown based on the information available and the nature of this type of device submission:

1. Table of Acceptance Criteria and Reported Device Performance

As a general PACS workstation for viewing, manipulating, communicating, and storing medical images, the “acceptance criteria” are primarily conformance to established standards and ensuring functional equivalence to the predicate. Specific quantitative diagnostic performance metrics are not typically required for a device of this classification.

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

• Sample Size: Not specified. Performance testing for a PACS workstation typically involves extensive internal verification and validation with a wide variety of anonymized clinical images rather than a single "test set" with a defined sample size for a specific clinical endpoint.
• Data Provenance: Not specified. Given it's a Siemens product developed in Germany, it's highly likely that testing data would include a mix of internal, international, and potentially U.S. clinical data, if specific clinical data were used, but the document does not elaborate. This type of submission relies more on functional testing and adherence to standards than a specific clinical dataset.

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

Not applicable. This is not a diagnostic AI/CAD device requiring expert-established ground truth for clinical performance evaluation. The "ground truth" for a PACS workstation is determined by its ability to accurately display, process, and transmit medical images as per DICOM standards and clinical requirements, which relies on engineering and software validation, not expert medical adjudication of diagnoses.

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

Not applicable. See point 3.

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. This is not an AI-assisted diagnostic device. It is a general PACS workstation. Therefore, an MRMC study and AI assistance effect size are not relevant to this submission.

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

No specific "standalone algorithm performance" study is described because the device is an interactive workstation designed for human users. The performance is assessed in terms of its functionality in supporting human users, not as an autonomous diagnostic algorithm.

7. The type of ground truth used

Not applicable in the sense of expert consensus, pathology, or outcomes data for a diagnostic performance evaluation. The "ground truth" for this device relates to the fidelity and accuracy of image display, processing, and storage according to technical specifications and DICOM standards. This is verified through technical validation, not clinical ground truth.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a "training set" in the context of machine learning model development. This is a conventional software application (PACS).

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

Not applicable. See point 8.

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The syngo Expert-I option for all Siemens CT systems is intended to allow a remote access to the syngo CT workplace from clients inside the local area network.

The syngo Expert-I option allows the local user of the syngo CT workplace to get help and assistance from other personnel on the local area network (LAN) to perform an evaluation of images faster and more efficiently. For this purpose, a remote user within the LAN can log on to the syngo CT workplace.

The provided document is a 510(k) summary for syngo Expert-I. It primarily describes a remote access software for CT systems and confirms its substantial equivalence to a previously cleared device. However, it does not contain any information regarding acceptance criteria, performance studies, sample sizes, expert qualifications, or ground truth establishment relevant to an AI/ML device.

Therefore, I cannot provide the requested information. The document focuses on administrative and regulatory aspects rather than detailed technical validation of a device's performance.

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