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The Cios Flow is a mobile X-Ray system designed to provide X-ray imaging of the anatomical structures of patient during clinical applications. Clinical applications may include but are not limited to: interventional fluoroscopic, gastro-intestinal, endoscopic, urologic, pain management, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The patient population may include pediatric patients.

The Cios Flow (VA30) mobile fluoroscopic C-arm X-ray System is designed for the surgical environment. The Cios Flow provides comprehensive image acquisition modes to support orthopedic and vascular procedures. The system consists of two major components:

• a) The C-arm with X-ray source on one side and the flat panel detector on the opposite side. The c-arm can be angulated in both planes and be lifted vertically, shifted to the side and move forward/backward by an operator.
• b) The second unit is the image display station with a moveable trolley for t processing and storage system, image display and documentation. Both units are connected to each other with a cable.
  The main unit is connected to the main power outlet and the trolley is connected to a data network.

The provided text describes modifications to an existing medical device, the Cios Flow (VA30) mobile fluoroscopic C-arm X-ray System, and seeks substantial equivalence to previously cleared predicate devices. The documentation focuses on demonstrating that the new features and changes do not introduce new safety or effectiveness concerns, primarily through a comparison of technological characteristics and compliance with recognized standards.

Here's an analysis of the acceptance criteria and study information based on the provided text:

Key Takeaway: The submission leverages a "Special 510(k)" pathway, implying that the changes made to the device are primarily modifications to existing features or the incorporation of features already cleared in other Siemens devices. Therefore, the "study" demonstrating performance is primarily a comprehensive set of non-clinical tests and verification/validation activities rather than a traditional large-scale clinical trial with human-in-the-loop assessments of diagnostic accuracy.

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to demonstrating that the modified device remains as safe and effective as its predicate devices, complying with relevant electrical safety, performance, and electromagnetic compatibility standards, and that all software specifications meet their defined criteria.

Table of Acceptance Criteria and Reported Device Performance:

Study Information (Based on the provided text):

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

   • The document does not specify a "test set" in terms of patient data or image datasets with a defined sample size for evaluating AI/algorithm performance. It focuses on non-clinical performance testing of the device's components and integrated system.
   • The provenance of data for these non-clinical tests is internal to Siemens (e.g., Siemens Healthcare GmbH Corporate Testing Laboratory). There is no mention of external data or patient data being used for a clinical performance evaluation of the new features that would typically involve a test set.

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

   • This information is not provided as the submission relies on non-clinical testing and comparison to predicate devices, not on a new clinical study requiring ground truth established by medical experts for diagnostic accuracy.
   • For the Human Factor Usability Validation, it mentions "customer employees are adequately trained in the use of this equipment" and "clinical use tests with customer report and feedback form." This implies user feedback, but not expert ground truth for interpretation.

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

   • Not applicable (None specified). No clinical study for diagnostic accuracy (e.g., using images interpreted by humans) is described, hence no adjudication method is detailed.

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 evidence of an MRMC study outlined for this submission. The changes described (software features like Target Pointer, DCM, Cios OpenApps, interactive touch control, new sound radiation delay, etc., and hardware like new detector, wireless footswitch, anti-microbial coating) are functional enhancements or component upgrades rather than a new AI diagnostic algorithm. Therefore, an MRMC study is not relevant to this submission as described.

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

   • Implicitly, standalone functional testing was done for the software components. The document states, "Performance tests were conducted to test the functionality of Cios Flow (VA30) System," and "Software Documentation for a Moderate Level of Concern software... The performance data demonstrates continued conformance with special controls for medical devices containing software. Nonclinical tests were conducted on Cios Flow (VA30) during product development." This describes testing the software and hardware components (including functions like Target Pointer, DCM, interactive controls) to ensure they operate as intended and meet specifications, but not in the context of an "algorithm only" diagnostic accuracy study.

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

   • For the non-clinical tests, the "ground truth" is adherence to engineering specifications, regulatory standards (e.g., electrical safety, EMC, software standards), and functional requirements. There is no mention of ground truth based on medical diagnoses, pathology, or outcomes data, as this is not a diagnostic AI device submission but a modification to an existing X-ray system.

7. The sample size for the training set:

   • Not applicable (None specified). The document does not describe a machine learning model that would require a distinct training set for diagnostic classification or prediction. The software modifications enhance the system's operational capabilities, not its diagnostic interpretation.

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

   • Not applicable. As no training set for a diagnostic AI model is discussed, the method for establishing its ground truth is not relevant to this submission.

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