Search Results

The Cios Alpha 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, gastrointestinal, endoscopic, urologic, pain management, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The patient population may include pediatric patients.

The Cios Alpha (VA30) mobile fluoroscopic C-arm X-ray System is designed for the surgical environment. The Cios Alpha 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 the image 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 Siemens Cios Alpha (VA30) is a mobile X-ray system. This submission describes modifications to the predicate device software.

Here's a breakdown of the acceptance criteria and study information:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria for each software modification with corresponding device performance metrics. Instead, it states that "The Cios Alpha software (VA30) was tested and found to be safe and effective for intended users, uses and use environments through the design control verification and validation process." and "The testing results support that all the software specifications have met the acceptance criteria."

The modifications are primarily software enhancements aimed at improving user interaction and safety features. The "reported device performance" is qualitative, asserting that the modified features function as intended and are comparable to or improved from the predicate devices.

2. Sample size used for the test set and the data provenance (country of origin of the data, retrospective or prospective)

The document does not specify the sample size for any test set or the data provenance (country, retrospective/prospective). It mentions "verification and validation testing" and "clinical use tests with customer report and feedback form" but provides no details on these.

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

The document does not specify the number or qualifications of experts used to establish ground truth for any test set.

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

The document does not mention any specific adjudication method for the test set.

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 MRMC comparative effectiveness study is mentioned in the provided text, nor is any AI component or human reader improvement with AI assistance discussed. The device is an X-ray system with software modifications, not an AI-powered diagnostic tool for interpretation.

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

This is not applicable as the device is an X-ray system, not a standalone algorithm. The "software modifications" refer to direct user interface and system control features, not an independent algorithm for diagnostic interpretation.

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

The document does not explicitly state the type of ground truth used. However, given the nature of the software modifications (Target Pointer, Interactive User Touch Control, Dose Regulation Indicator, Sound Radiation Delay, Product Software Security), the ground truth for their effectiveness would likely be based on functional testing, compliance with specifications, and user feedback/system behavior validation, rather than medical ground truth like pathology or outcomes data. The Human Factor Usability Validation suggests user feedback played a role.

8. The sample size for the training set

This is not applicable since the modifications are for a medical device's operating software, not for an AI/machine learning algorithm that requires a training set.

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

This is not applicable for the same reason as point 8.

Ask a specific question about this device