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The Cios Select is a mobile X-ray system intended for use in Operating room, Traumatology, Endoscopy, Intensive Care Station, Pediatrics, Ambulatory patient care and in Veterinary Medicine.

The Cios Select can operate in four different modes, Digital Radiography, Fluoroscopy and Cassette exposure which are necessary in performing wide variety of clinical procedures, such as intraoperative bile duct display, fluoroscopic display of an intra-medullary nail implants in various positions, low dose fluoroscopy in pediatrics, fluoroscopic techniques utilized in pain therapy and positioning of catheters and probes.

Theis 510(k) submission, Cios Select (VA21C) with Imagine Intensifier is a Mobile Carm X-ray System, which is a modification of the Cios Select (VA21) Image Intensifier originally cleared under Premarket Notification K210307 on March 04, 2021.

The Cios Select consists of two major units:

One is the acquisition unit with the C-arm and movable base containing the generator, power unit, system control, and tube housing assembly on one side of the C-arm and the image intensifier on the opposite side.

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 describes a 510(k) premarket notification for a medical device called "Cios Select (VA21) Image Intensifier." This is a special 510(k) submission, meaning it concerns minor modifications to an already cleared predicate device (K210307). As such, the focus of the documentation is on demonstrating that the modified device remains substantially equivalent to the predicate, rather than proving novel performance.

Therefore, the document does not contain a typical "study" proving the device meets acceptance criteria in the sense of a clinical trial or performance study against specific, quantified metrics with a defined ground truth and expert review. Instead, the "acceptance criteria" are implied by compliance with safety and performance standards for an image-intensified fluoroscopic x-ray system, and the "study" is a series of verification and validation (V&V) tests to confirm that the modifications do not introduce new safety or effectiveness issues.

Based on the provided information, here's a breakdown:

1. Table of Acceptance Criteria and Reported Device Performance:

Since this is a special 510(k) for modifications, the "acceptance criteria" are primarily related to maintaining safety and performance equivalence to the predicate device and adhering to recognized standards. Specific quantitative performance metrics are not explicitly listed in a table format for the device's image quality or diagnostic accuracy in this submission narrative.

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

• Test Set Sample Size: Not explicitly stated as a separate "test set" in the context of diagnostic performance involving patient data. The "tests" mentioned are non-clinical (bench testing, verification, and validation).
• Data Provenance: The study described is primarily non-clinical validation and verification. There is no indication of patient data being used for the performance evaluation in this specific 510(k) submission, as it relates to system modifications and demonstrating substantial equivalence. The predicate device's prior clearance would have relied on such data. Since clinical testing was deemed "not necessary" for this special 510(k), no patient data (retrospective or prospective, or country of origin) is specified for the current submission's performance evaluation.

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

• Not applicable as this submission describes non-clinical testing for substantial equivalence, not a diagnostic performance study requiring expert ground truth for patient data.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set:

• Not applicable for the same reason as above.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

• No, an MRMC study was not performed. This device is an image intensifier system (mobile C-arm X-ray), not an AI-based diagnostic algorithm that assists human readers.

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

• No, this is a medical imaging hardware system with integrated software, not a standalone AI algorithm. The performance evaluation focuses on the safety and functionality of the modified system.

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

• For the non-clinical tests described, the "ground truth" is defined by established engineering specifications, performance standards (e.g., IEC, AAMI, CFR), and functional requirements rather than medical ground truth like pathology or expert consensus on clinical cases. The tests verify that the device performs according to these engineering and regulatory specifications.

8. The Sample Size for the Training Set:

• Not applicable. This is not an AI model that underwent a separate and distinct training phase using a specific dataset. The "software" updates mentioned are for system control and image processing, not for a trainable machine learning model.

9. How the Ground Truth for the Training Set was Established:

• Not applicable for the same reason as above.

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The Cios Select is a mobile X-ray system intended for use in Operating room. Traumatology, Endoscopy, Intensive Care Station, Pediatrics, Ambulatory patient care and in Veterinary Medicine.

The Cios Select can operate in four different modes, Digital Radiography, and Pulsed Fluoroscopy and Cassette exposure which are necessary in performing wide variety of clinical procedures, such as intraoperative bile duct display, fluoroscopic display of a intra-medullary nail implants in various positions, low dose fluoroscopy in pediatrics, fluoroscopic techniques utilized in pain therapy and positioning of catheters and probes.

This 510(k) submission, Cios Select (VA21) is a Mobile C-arm X-ray System. The Cios Select (VA21) is a modification of the Cios Select originally cleared under Premarket Notification K153232 on February 10, 2016.

The Cios Select consists of two major units:

One is the acquisition unit with the C-arm and movable base containing the generator, power unit, system control, and tube housing assembly on one side of the C-arm and the image intensifier on the opposite side.

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 Cios Select (VA21) Image Intensifier, which describes modifications to an existing mobile X-ray system. The document focuses on demonstrating substantial equivalence to predicate devices through verification and validation of these modifications. It primarily references non-clinical performance testing and compliance with various standards.

Here's a breakdown of the requested information based on the provided text:

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

The document does not provide a specific table of quantitative acceptance criteria and corresponding reported device performance values in the way one might expect for a diagnostic AI device. Instead, it details that the device underwent non-clinical performance testing to ensure compliance with several industry standards and regulations.

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

The document primarily discusses non-clinical performance testing and mentions "Bench test Summaries and System Verification and Validation testing." There is no mention of a "test set" in the context of clinical images or data provenance (country of origin, retrospective/prospective). The evaluations were primarily conducted in a laboratory or manufacturing setting.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This submission relies on engineering and regulatory compliance testing rather than clinical expert review of images for ground truth establishment.

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

Not applicable. As noted above, this submission does not involve a test set requiring expert adjudication for ground truth.

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 study was mentioned. The device is an image intensifier system, not an AI-powered diagnostic aid that assists human readers.

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

This refers to an X-ray imaging system, not an algorithm. The "standalone" performance refers to the system's ability to operate according to its specifications and regulatory standards. The documentation indicates that "Performance tests were conducted to test the functionality of Cios Select (VA21) System," implying standalone performance evaluation of the device itself.

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

For the non-clinical testing, the "ground truth" is defined by the technical specifications outlined in the various industry standards (e.g., IEC, AAMI) and regulatory requirements (e.g., 21 CFR sections) to which the device was tested for compliance.

8. The sample size for the training set

Not applicable. The document describes an X-ray imaging device and its software modifications, not a machine learning or AI algorithm that requires a "training set."

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

Not applicable, as no training set for a machine learning algorithm is discussed in the provided text.

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