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The Cios Spin 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 Spin (VA30) mobile fluoroscopic C-arm X-ray System is designed for the surgical environment. The Cios Spin 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 provided text is a 510(k) summary for the Siemens Cios Spin X-ray system, specifically concerning modifications to its software (VA30 version). This document focuses on demonstrating substantial equivalence to existing predicate devices, rather than proving that the device meets specific patient outcome or diagnostic performance acceptance criteria typical for AI/ML devices or new medical technologies.

Therefore, the document does not contain the information requested in most of the points about acceptance criteria and study design for proving device performance in terms of clinical accuracy or effectiveness (e.g., accuracy metrics, sample sizes for test sets, expert ground truth establishment, MRMC studies, standalone performance, training set details).

This 510(k) serves to show that the modified Cios Spin (VA30) is as safe and effective as its predicate devices, primarily through nonclinical performance testing, software verification/validation, and compliance with recognized standards. The modifications listed are largely related to user interface enhancements and software security, not core image acquisition or diagnostic capabilities that would require extensive clinical accuracy studies.

However, I can extract information related to the testing performed to demonstrate substantial equivalence for the listed software modifications.

Here's a breakdown based on the available information:

1. A table of acceptance criteria and the reported device performance:

The document does not provide a table of acceptance criteria for diagnostic performance metrics (e.g., sensitivity, specificity, AUC) or human reader improvement with AI assistance, as it is a 510(k) for a modified X-ray system, not a new diagnostic AI algorithm.

Instead, the acceptance criteria for these modifications appear to be:

• Software Functionality: The new software features (Target Pointer, Interactive User Touch Control, Dose Regulation Indicator, Sound Radiation Delay, Product Software Security, Screw removal button) function as intended and do not introduce new safety or effectiveness issues.
• Compliance with Standards: The device complies with relevant electrical safety, performance, electromagnetic compatibility (EMC), and software standards (e.g., AAMI ANSI ES60601-1, IEC 60601-1-2, IEC 62304, ISO 14971, etc.).
• Risk Mitigation: Identified risks are mitigated through design controls, verification, and validation.
• Human Factors/Usability: The device is safe and effective for intended users and use environments, and human factors are adequately addressed.

The "reported device performance" for these criteria is stated as:

• "The Cios Spin (VA30) was certified by Siemens Healthcare GmbH Corporate Testing Laboratory to comply with the following standards..." (lists standards).
• "The performance data demonstrates continued conformance with special controls for medical devices containing software."
• "The Risk analysis was completed, and risk control implemented to mitigate identified hazards. The testing results support that all the software specifications have met the acceptance criteria."
• "Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence."
• "The Cios Spin 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."
• "The Human Factor Usability Validation showed that Human factors are addressed in the system test according to the operator's manual and in clinical use tests with customer report and feedback form."
• "Results of all conducted testing and clinical assessment were found acceptable and do not raise any new issues of safety or effectiveness."

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

• Sample Size: The document does not specify a sample size for a "test set" in the context of diagnostic performance (e.g., number of images, number of patients). The testing described is primarily non-clinical verification and validation of software functionality and compliance with standards. It mentions "system test" and "clinical use tests with customer report and feedback," but no specific numerical sample sizes for these.
• Data Provenance: Not applicable as there's no diagnostic performance test set described. The testing described would be performed internally by Siemens (e.g., "Siemens Healthcare GmbH Corporate Testing Laboratory").

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

• Not Applicable. This document describes modifications to an X-ray imaging system's software features (e.g., user interface, security) and not a diagnostic AI algorithm that would require human expert ground truth for its performance evaluation (e.g., radiologists labeling findings).

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, an MRMC study was not done. This type of study is relevant for AI-assisted diagnostic tools, not for the software modifications described for this X-ray system. The document focuses on the system's functionality and safety.

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

• Not Applicable. The described "modifications" are software features integrated into an X-ray system, meant to be used by a human operator (e.g., "Target Pointer", "Interactive User Touch Control"). There isn't a standalone diagnostic algorithm being evaluated.

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

• Not Applicable. For the software modifications, "ground truth" would be the functional specification and expected safe operation of the software feature itself, verified through engineering tests and user testing, rather than a clinical ground truth like pathology or expert consensus on a diagnosis.

8. The sample size for the training set:

• Not Applicable. There is no mention of a "training set" as this is not an AI/ML device that requires machine learning for its primary function. The software modifications are deterministic functional changes.

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

• Not Applicable. See point 8.

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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, 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 Alpha mobile fluoroscopic C-arm X-ray System is designed for the surgical environment. The Cios Alpha provides comprehensive image acquisition modes. The system consists of two major components:
a) The C-arm with an 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 lifted vertically, shifted to the side and moved forward/backward by an operator.
b) The second component is the image display station with a moveable trolley that holds the image processing and storage system, and the image display. Both components are connected to each other with a cable.

Here's an analysis of the acceptance criteria and study information provided in the document for the Cios Alpha (VA30) device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for quantitative performance metrics in a pass/fail format. Instead, it presents a comparison of the Subject Device's (Cios Alpha (VA30)) performance to its Predicate (Cios Alpha (VA10)) and Reference Devices for Solid State X-Ray Imaging (SSXI) specifications. The implication is that comparable or better performance is the acceptance criterion for the SSXI metrics.

Additional Acceptance Criteria (General):

• Compliance with voluntary standards (Table 3), FDA Guidance Documents (Table 4).
• Software verification and validation meeting acceptance criteria.
• Risk analysis completed and hazards mitigated.
• Human Factors Usability Validation showing addressing human factors and successful clinical use tests.
• Cybersecurity requirements met.

Study Proving Device Meets Acceptance Criteria:

The document describes several non-clinical performance tests and analyses to demonstrate that the Cios Alpha (VA30) meets the acceptance criteria, primarily for substantial equivalence to its predicate devices.

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

• Test Set Sample Size: The document does not specify a distinct "test set" sample size in terms of patient data or number of images for evaluating the SSXI metrics. The performance evaluation seems to be based on engineering bench testing of device capabilities rather than a separate clinical image set.
• Data Provenance: The data provenance for the SSXI metrics and other performance tests is non-clinical bench testing. The document states: "Performance tests were conduct[ed] to test the functionality of the Cios Alpha (VA30)." It also mentions "Additional engineering bench testing was performed including: the non-clinical testing identified in the guidance for submission of 510(k) s for Solid State X-Ray Imaging Devices (SSXI); demonstration of system performance; and an imaging performance evaluation."
  • The "clinical images are not required" statement further confirms the non-clinical nature of the specific SSXI evaluation.
  • The Human Factor Usability Validation mentions "clinical use tests with customer report and feedback form," which implies some level of prospective, real-world (or simulated real-world) interaction, but specific sample sizes are not provided.
  • The origin of the data is Siemens Healthcare GmbH Corporate Testing Laboratory and internal verification/validation processes.

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

• The document does not describe the use of experts to establish ground truth for a test set in the traditional sense of image interpretation for diagnostic accuracy. The testing primarily focuses on technical specifications of the imaging system itself.
• For the Human Factors Usability Validation, "customer report and feedback form" are mentioned, implying input from users (healthcare professionals), but no specific number or detailed qualifications are provided.

4. Adjudication Method for the Test Set

• Given that the primary performance evaluation described is non-clinical bench testing of engineering specifications (SSXI metrics), an adjudication method for a test set based on expert consensus would not be applicable or mentioned. The "ground truth" for these metrics is objectively measured device performance.

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. This submission is for an imaging system (C-arm X-ray system), not an AI-powered diagnostic algorithm that assists human readers. While it includes "new software functions" like "Target Pointer," which "enables the automatic detection of K-wires and displays the trajectory," the document does not present a study evaluating the impact of this feature on human reader performance or diagnostic accuracy.

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

• The document evaluates the Cios Alpha as an imaging system, not a standalone AI algorithm. While it contains new software features, the performance metrics discussed (e.g., DQE, MTF) are system-level imaging characteristics. The "Target Pointer" feature performs automatic detection, but its standalone performance (e.g., accuracy of K-wire detection) is not detailed in the provided text. The overall context is regulatory clearance for hardware and software modifications of an existing medical device, not a new AI-enabled diagnostic device undergoing standalone performance evaluation.

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

• For the SSXI performance metrics (DQE, Dynamic Range, MTF, etc.), the "ground truth" is based on objective physical measurements and technical standards. These are inherent properties of the imaging system's detector and processing.
• For software functions, "ground truth" is established through detailed software testing to confirm they "worked as intended" according to specifications and requirements.
• For Human Factors, ground truth would relate to usability and safety observations and feedback during "clinical use tests."

8. The Sample Size for the Training Set

• The document does not mention a training set sample size. This is expected as the submission primarily concerns an imaging system rather than a machine learning algorithm requiring a distinct training phase with annotated data. Although new software features are present, the submission focuses on their validation as part of the overall device.

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

• Since no training set is discussed, the method for establishing its ground truth is also not applicable in this document.

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