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The Cios Spin is a mobile X-ray system designed to provide X-ray imaging of the anatomical structures of patients 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 (VA31A) 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 following modifications were made to the Predicate Device the Cios Spin Mobile X-ray System cleared under Premarket Notification K210054 on February 5, 2021. Siemens Medical Solutions USA, Inc. submits this Traditional 510(k) to request clearance for the Subject Device Cios Spin (VA31A). The following modification is incorporated in the Predicate Device to create the Subject Device, for which Siemens is seeking 510(k) clearance:

1. Software updated from VA30 to VA31A to support the below software features
   A. Updated Retina 3D for optional enlarged 3D Volume of 25cm x 25cm x 16cm
   B. Introduction of NaviLink 3D Lite
   C. Universal Navigation Interface (UNI)
   D. Updated InstantLink with Extended NXS Interface
2. Updated Collimator
3. Updated FLC Imaging system PC with new PC hardware Updated AppHost PC with High Performance Graphic Card
4. New Eaton UPS 5P 850i G2 as successor of UPS 5P 850i due to obsolescense

Based on the provided FDA 510(k) clearance letter for the Siemens Cios Spin (VA31A), here's an analysis of the acceptance criteria and the study proving the device meets them:

Important Note: The provided document is a 510(k) summary, which often summarizes testing without providing granular details on study design, sample sizes, and ground truth establishment to the same extent as a full clinical study report. Therefore, some information requested (e.g., specific number of experts for ground truth, adjudication methods) may not be explicitly stated in this summary. The focus of this 510(k) is primarily on demonstrating substantial equivalence to a predicate device, especially for software and hardware modifications, rather than a de novo effectiveness study.

Acceptance Criteria and Reported Device Performance

The 510(k) summary primarily focuses on demonstrating that the modifications to the Cios Spin (VA31A) do not introduce new safety or effectiveness concerns compared to its predicate device (Cios Spin VA30) and a reference device (CIARTIC Move VB10A) that incorporates some of the new features. The acceptance criteria are implicitly tied to meeting various industry standards and demonstrating functionality and safety through non-clinical performance testing.

Table 1: Acceptance Criteria and Reported Device Performance

Study Details Proving Device Meets Acceptance Criteria

The study described is primarily a non-clinical performance testing and software verification and validation effort rather than a traditional clinical trial.

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

   • Test Set Sample Size: Not explicitly stated as a "sample size" in the context of patients or images for performance evaluation. The testing described is "Unit, Subsystem, and System Integration testing" and "software verification and regression testing." This type of testing uses a diverse set of test cases designed to cover functionality, performance, and safety requirements. For the "Enlarged Volume Field of View," it's a non-clinical test, likely using phantoms or simulated data.
   • Data Provenance: Not applicable in terms of patient data provenance for the non-clinical and software testing described. This is bench testing and software validation. Customer reports and feedback forms are mentioned for human factors, but specific details on their origin (country, etc.) are not provided. The manufacturing site is Kemnath, Germany.

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

   • Not explicitly stated. For non-clinical performance and software testing, "ground truth" is typically established by engineering specifications, known correct outputs for given inputs, and compliance with industry standards. If clinical use tests involved subjective evaluation, the number and qualifications of experts are not detailed, but they are implied to be "healthcare professionals" (operators are "adequately trained").

3. Adjudication method for the test set:

   • Not applicable/Not explicitly stated. For software and bench testing, adjudication usually refers to a process of resolving discrepancies in ratings or measurements. Given the nature of this submission (software/hardware modifications and non-clinical testing), formal clinical adjudication methods (like 2+1, 3+1 for image reviews) are not described as part of the primary evidence. Acceptance is based on test cases meeting predefined engineering requirements.

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 study was not conducted. This 510(k) is for a mobile X-ray system with software and hardware updates, not an AI-assisted diagnostic device where evaluating human reader performance with and without AI would be relevant. The "AI" mentioned (Retina 3D, NaviLink 3D) refers to advanced imaging/navigation features, not machine learning for diagnostic interpretation.

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

   • Yes, implicitly. The "non-clinical test 'Enlarged Volume Field of View' testing" and other "Unit, Subsystem, and System Integration testing" for functionality and performance are essentially standalone tests of the device's components and software without immediate human interpretation in a diagnostic loop. The acceptance criteria for these tests refer to technical performance endpoints, not diagnostic accuracy.

6. The type of ground truth used:

   • Engineering Specifications and Standard Compliance: For the performance and safety testing, the "ground truth" is adherence to predefined engineering requirements (e.g., image dimensions, system response times, electrical safety limits) and compliance with national and international industry standards (e.g., IEC 60601 series, ISO 14971, NEMA PS 3.1).
   • For the Human Factors Usability Validation, "customer reports and feedback forms" serve as a form of "ground truth" regarding user experience and usability.

7. The sample size for the training set:

   • Not applicable. This submission describes modifications to an X-ray imaging system, not the development of a machine learning algorithm that requires a separate training set. The existing software (VA30) was updated to VA31A. The "training" for the software itself would have occurred during its initial development, not for this specific 510(k) submission.

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

   • Not applicable. As above, this information is not relevant to this specific 510(k) submission, as it focuses on modifications to an existing device rather than the development of a new AI/ML algorithm requiring a training set and its associated ground truth.

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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 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 mobile fluoroscopic C-arm X-ray System 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.

This document describes the premarket notification (510(k)) for the Siemens Cios Spin X-ray system. The information provided is primarily focused on demonstrating substantial equivalence to predicate devices, rather than a standalone clinical study with detailed acceptance criteria for a new AI feature.

However, based on the provided text, we can infer acceptance criteria and the studies performed for specific features, particularly those listed under "New Software VA30 due to new functionality."

Summary of Device and Context:
The Cios Spin is a mobile fluoroscopic C-arm X-ray system for imaging anatomical structures during various clinical applications, including interventional, orthopedic, and neurological procedures. The 510(k) submission highlights several modifications and new software functionalities compared to its predicate device, the Cios Alpha.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a dedicated section for the new software features. Instead, it relies on comparative equivalence and verification/validation testing against established guidance and predicate device performance. For the detector, quantitative metrics are provided.

• Conformance to FDA Recognized Consensus Standards and Guidance Documents.
• Software specifications meet acceptance criteria.
• Risk analysis completed and controls implemented for identified hazards.
• Safe and effective for intended users, uses, and environments (through design control V&V). | - Complies with 21 CFR 1020.30, 1020.32.
• Certified to comply with AAMI ANSI ES60601-1:2005/(R)2012, IEC 60601-1-2:2014, IEC 60601-1-3:2013, IEC 60601-1-6:2010/A1:2013, IEC 60825-1:2014, IEC 62304:2015, IEC 60601-2-28:2010, IEC 60601-2-43:2017, IEC 60601-2-54:2009/A1:2015, ISO 14971:2007, IEC 62366-1:2015/Cor1:2016.
• Verification and validation testing found acceptable, supporting claims of substantial equivalence.
• All new software functions validated; worked as intended.
• Human Factor Usability Validation showed human factors addressed, with adequate training for employees.
• Cybersecurity statement provided, considering IEC 80001-1:2010. |
  | New Software (e.g., Metal Artifact Reduction, Retina 3D, Screw Scout, Target Pointer, High Power 3D, Easy 3D) | - Does not raise any new issues of safety or effectiveness.
• Works as intended (for new software functions). | - For Metal Artifact Reduction: The algorithm is unchanged from a previously cleared device (syngo Application Software VD20B, K170747). Improves image quality by reducing artifacts.
• For Retina 3D, Screw Scout, Target Pointer, Cios Open Apps: Non-clinical testing and Software Verification/Validation testing conducted and acceptable per Software Guidance document. Retina 3D has the same reconstruction algorithm as predicate ARTIS Pheno.
• For High Power 3D & Easy 3D: Does not raise any new issues of safety or effectiveness per Software Guidance. |
  | CMOS Flat Panel Detector | - Equivalent image quality to a-Si technology detector.
• Does not raise any new issues of safety or effectiveness.
• Compliance with "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" for performance metrics. | - DQE: 72% (vs. Predicate Cios Alpha 76%, Reference Ziehm Solo FD 70%)
• Dynamic Range: 96dB (vs. Predicate Cios Alpha 94dB, Reference Ziehm Solo FD Equivalent)
• MTF: 58% at 1 lp/mm (large) (vs. Predicate Cios Alpha 55% at 1 Lp/mm, Reference Ziehm Solo FD 4lp/mm)
• Digitization Depth: 16 bit (same as predicates/references)
• Pixel Pitch: 152 μm (vs. Predicate Cios Alpha 194μm, Reference Ziehm Solo FD 100 μm)
• Field of View: 30 cm x 30 cm; 20 cm x 20 cm |

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

The document does not specify a "test set" in terms of patient data for evaluating the new software features. The testing mentioned is primarily non-clinical performance testing, software verification and validation (V&V), human factors usability validation, and engineering bench testing.

• Sample Size: Not applicable in the context of patient data for the new software features, as the testing described is primarily technical and comparative against existing standards and predicate devices. For the detector, the metrics (DQE, MTF, etc.) are derived from laboratory measurements, not patient data sets.
• Data Provenance: Not specified as patient data is not the primary focus for the equivalence argument. The testing was conducted by Siemens Healthcare GmbH Corporate Testing Laboratory (for conformance standards) and internally for software V&V. This implies internal company testing, likely in a controlled laboratory environment.

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

Given that the testing is primarily non-clinical and focused on technical performance and software functionality, the concept of "ground truth established by experts" for a patient-based test set is not directly applicable in the way it would be for an AI diagnostic algorithm.

• Experts: The "experts" involved are implied to be the engineers and technical specialists responsible for conducting the non-clinical tests, software verification/validation, and human factors evaluations. The approval by the FDA also involves review by regulatory experts.
• Qualifications: While not explicitly stated, these would be Siemens' internal development and quality assurance teams, as well as external certification bodies for standards compliance.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a patient-based test set requiring expert adjudication for ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done for the Cios Spin in this 510(k) submission. The submission focuses on demonstrating substantial equivalence through technological characteristics, non-clinical performance data, and software validation. It does not include an assessment of how human readers improve with or without AI assistance.

6. Standalone (Algorithm Only Without Human-In-The-Loop Performance) Study

The document does not describe a standalone performance study for the software features (e.g., Metal Artifact Reduction, Retina 3D, Screw Scout, Target Pointer) similar to what would be done for a diagnostic AI algorithm. Instead, it states that "All new software functions present in the Subject Device... have been validated through detailed software testing and it was founded they worked as intended." This implies internal functional and performance testing, but not a standalone clinical performance study typically associated with AI algorithms.

7. Type of Ground Truth Used

The "ground truth" for the various new features is established through:

• Engineering specifications and design requirements: For software functionality and hardware performance.
• Compliance with recognized industry standards: (e.g., IEC standards, FDA performance standards)
• Comparison to predicate devices and reference devices: For performance metrics (e.g., DQE, MTF for the detector), where "equivalent" or "comparable" performance serves as the ground truth.
• Expected "working as intended" functionality: For the new software features validated through detailed software testing.

There is no mention of pathology, expert consensus on patient cases, or outcomes data used to establish ground truth for the specific performance of these new features in a clinical setting.

8. Sample Size for the Training Set

Not applicable. The document describes a medical device (X-ray system) with new software features, not a machine learning model that requires a "training set" in the context of AI/ML development. The software validation is based on internal testing against specifications.

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

Not applicable, as there is no "training set" as understood in machine learning. The "ground truth" for the software validation mentioned in the document is based on meeting pre-defined software specifications and functional requirements through verification and validation testing.

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