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CUVIS-spine is intended for use as an aid for precisely locating anatomical structures and for the spationing and orientation of guide bush to be used by surgeons for navigating compatible surgical instruments in open or percutaneous pedicle screw placement provided that the required markers and rigid patient anatomy can be identified on 3D or 2D image.

The CUVIS-spine is a mobile system mainly comprising the robotic arm, the main console and the staff console as an option. The robotic arm is positioned on the floor near the side of the surgical table. The location of the main console or the staff console is appropriately determined considering the user preference and the environments.

CUVIS-spine is a pedicle screw guide system which consists of Robotic Arm, Main Console, Staff Console, Guide bush, Source Calibrator, Registration Tool, Registration Tool Adapter, Drape, Marker Ball, Patient Marker, Clamp, Adapter, Pin, Pin vise, Slide Hammer, Marker Driver, Detector calibrator, Dilator, Serration-tip dilator, Bur, Awl, Probe, Lenke probe, Tapper, Stylet tapper, Screwdriver, Instrument container.

The provided text describes the CUVIS-spine device, a robotic-assisted pedicle screw placement system. However, it does not contain specific acceptance criteria or details of a study demonstrating that the device meets those criteria, particularly regarding clinical performance metrics like accuracy measurements, clinical outcomes, or human reader effectiveness.

The document primarily focuses on:

• Substantial Equivalence: Comparing the CUVIS-spine to predicate devices (CUVIS-spine K201569 and Excelsius GPS™ K171651) based on technical characteristics, indications for use, and general device descriptions.
• Non-Clinical Tests: Mentioning that pose accuracy and repeatability were tested, and a cadaveric study was performed. It also lists the consensus standards used for biocompatibility, electrical safety, electromagnetic compatibility, risk management, usability, and software validation (including cybersecurity).

Therefore, I cannot populate the table or provide detailed answers to most of your questions based on the input text. The information needed to describe acceptance criteria and a study proving the device meets them (e.g., specific accuracy metrics, clinical data, sample sizes for test sets, ground truth establishment, or human reader improvement) is not present.

Here's what can be inferred or stated based on the given text, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information for a Comprehensive Answer:

• Quantitative Acceptance Criteria: The document states that accuracy, pose accuracy, and repeatability were tested, but it does not define the specific numerical thresholds for these criteria (e.g., "accuracy must be within 1.0 mm").
• Quantitative Device Performance: Similarly, it does not report the actual measured performance values for accuracy, pose accuracy, or repeatability. It only states they were "tested and validated."

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

• Test Set Sample Size: The document mentions a "cadaveric study" for the proposed device but does not state the sample size (e.g., number of cadavers, number of pedicles analyzed).
• Data Provenance: The document does not specify the country of origin of the cadaveric study or any other test data. It indicates the study was "cadaveric," implying it's a prospective experimental study, but details are lacking.

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

• This information is not provided in the document.

4. Adjudication method for the test set

• This information is not provided in the document.

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

• A MRMC comparative effectiveness study is not mentioned in the document. The device is a robotic-assisted surgical system, not an AI diagnostic imaging tool that typically involves human reader performance studies in that context. The focus is on the robot's ability to aid surgeons in instrument placement.

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

• The document implies that "Pose accuracy and Repeatability of the CUVIS-spine were tested and validated," which would constitute standalone technical performance testing of the robotic system itself, independent of a human surgeon's real-time interaction beyond initial setup and guidance. However, no specific details or results are provided to elaborate on this.

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

• For the cadaveric study, the "ground truth" for pedicle screw placement accuracy would typically be established through post-procedure imaging (e.g., CT scans) quantitatively analyzed against the planned trajectory or anatomical landmarks. However, the document does not explicitly state how ground truth was established for the cadaveric study or the accuracy verification.

8. The sample size for the training set

• This information is not provided in the document. The document describes a robotic system, not a machine learning model, so a "training set" in the typical ML sense might not be applicable or explicitly mentioned here.

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

• This information is not provided in the document, as it does not discuss a training set.

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CUVIS-spine is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of guide bush to be used by surgeons for navigating compatible surgical instruments in open or percutaneous surgical procedures provided that the required markers and rigid patient anatomy can be identified on O-arm or C-arm.

The CUVIS-spine is a mobile system mainly comprising the robotic arm, the main console and the staff console as an option. The robotic arm is positioned on the floor near the side of the surgical table. The location of the main console or the staff console is appropriately determined considering the user preference and the environments. CUVIS-spine is a pedicle screw guide system which consists of Robotic Arm, Main Console, Staff Console, Guide bush, Source Calibrator, Registration Tool, Registration Tool Adapter, Robotic Arm Drape, Tool Drape, Detector Drape, Marker Ball, Patient Marker, Marker Driver, Detector Calibrator, Dilator, Serration-tip Dilator, Drill Bit, Tapper, Stylet Tapper, Screwdriver, Instrument Container.

Here's a breakdown of the acceptance criteria and study information for the CUVIS-spine device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on claiming substantial equivalence to a predicate device (CUVIS-spine K201569) rather than explicitly stating new, quantifiable acceptance criteria for performance metrics. However, it does mention that "Pose accuracy and Repeatability of the CUVIS-spine were tested and validated." Without specific numerical targets for these, a direct table of acceptance criteria vs. performance is not fully possible.

However, based on the non-clinical tests mentioned, we can infer performance was evaluated against general medical device safety and efficacy standards. The "Accuracy" row in the comparison table with the predicate device also indicates that the proposed device maintains accuracy.

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

• Sample Size for Test Set: The document mentions a cadaveric study was performed for the robotic-assisted pedicle screw placement. However, the sample size (number of cadavers or placement procedures) is not specified in the provided text.
• Data Provenance: The document does not explicitly state the country of origin for the cadaveric study data. It is a non-clinical, prospective study (as it was performed specifically for testing the proposed device).

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

This information is not provided in the document. For a cadaveric study of pedicle screw placement, ground truth would typically be established by imaging (e.g., post-procedure CT scans) reviewed by qualified medical professionals (e.g., orthopedic surgeons or radiologists). The number and qualifications of such experts are not detailed here.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, an MRMC comparative effectiveness study involving human readers with and without AI assistance is not mentioned in the document. The study described is a cadaveric study focused on physical performance and accuracy, not reader performance.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The device itself (CUVIS-spine) is a robotic-assisted surgical system, implying human interaction. The non-clinical tests focused on the device's "Pose accuracy and Repeatability," which are standalone measures of the robot's mechanical performance. However, there isn't a specific mention of an "algorithm only" study in the context of image interpretation or diagnosis separate from the physical robotic guidance. The cadaveric study inherently involves the algorithm driving the robot.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For the cadaveric study on robotic-assisted pedicle screw placement, an implied ground truth would likely be established through post-procedure imaging (e.g., CT scans) to assess the accuracy of screw placement relative to the planned trajectory and anatomical landmarks. This assessment would typically be performed by experts, but the specifics are not detailed.

8. The Sample Size for the Training Set

This device is a robotic surgical system, not an AI model that learns from large datasets in the traditional sense (e.g., for image classification). Therefore, a "training set" as understood in machine learning for diagnostic AI is not applicable in the context described in this document. The robotic system's "training" would involve mechanical calibration, software development, and validation against engineering specifications, not data-driven learning from patient cases in the same way.

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

As explained in point 8, a "training set" for an AI model learning from data is not described. The ground truth for the robotic system's development would be based on engineering specifications, physical measurements, and validation against known anatomical models and surgical planning parameters.

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CUVIS-spine is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of guide bush to be used by surgeons for navigating compatible surgical instruments in open or percutaneous pedicle screw placement provided that the required markers and rigid patient anatomy can be identified on O-arm or C-arm.

The CUVIS-spine is a mobile system mainly comprising the robotic arm, the main console and the staff console as an option. The robotic arm is positioned on the floor near the side of the surgical table. The location of the main console or the staff console is appropriately determined considering the user preference and the environments.

The CUVIS-spine is a pedicle screw guide system which consists of Robotic Arm, Main Console, Staff Console, Guide bush, Source Calibrator, Registration Tool, Registration Tool Adapter, Robotic Arm Drape, Tool Drape, Detector Drape, Marker Ball, Patient Marker, Marker Driver, Detector Calibrator, Dilator, Serration-tip Dilator, Drill Bit, Tapper, Stylet Tapper, Screwdriver, Instrument Container.

The provided text describes the CUVIS-spine device, a robotic-assisted pedicle screw placement system. It includes information on its regulatory classification, indications for use, and a comparison to a predicate device. However, it does not contain specific acceptance criteria or an explicit study proving that the device meets defined acceptance criteria in the format requested.

The Performance Data section mentions non-clinical tests for "pose accuracy and repeatability" and a "cadaveric study" for robotic-assisted pedicle screw placement. It also touches on biocompatibility, electromagnetic compatibility, electrical safety, risk management, and usability. However, it does not provide details on the acceptance criteria for these tests, nor the results in a quantified manner that directly addresses the prompt's request for a table of acceptance criteria and reported device performance.

Therefore, I cannot fully complete the requested table or describe a detailed acceptance criterion study based on the provided text.

Here's a breakdown of what can be extracted and what is missing:

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

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

• Sample size for non-clinical tests (pose accuracy, repeatability): Not specified.
• Sample size for cadaveric study: Not specified (referred to as "A cadaveric study").
• Data provenance: Not specified. The studies are referred to generally as "non-clinical tests" and "A cadaveric study," without details on the origin or nature (retrospective/prospective) of the data.

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

• This information is not provided in the text. The document refers to "testing and validation" and a "cadaveric study" but does not detail the methodology for establishing ground truth or the involvement/qualifications of experts.

4. Adjudication method for the test set

• This information is not provided in the text.

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

• This information is not provided in the text. The CUVIS-spine is described as a robotic-assisted system for surgical instrument guidance, not an AI software for interpretation by human readers. Therefore, an MRMC study related to human reading improvement with AI assistance would not be applicable in this context.

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

• The document implies a standalone performance component for the robotic system in its "pose accuracy and repeatability" tests and the cadaveric study, as these would assess the device's technical performance. However, it does not explicitly state "standalone performance study" or provide results that isolate the algorithm's performance without human interaction during operation. The system's indication for use is "as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of guide bush to be used by surgeons," implying human interaction is part of its intended use.

7. The type of ground truth used

• For the cadaveric study, the implied ground truth would likely be based on post-insertion imaging (e.g., CT scans) or physical dissection to verify screw placement accuracy relative to the surgical plan. However, this is not explicitly stated.
• For pose accuracy and repeatability, the ground truth would be precise measurements of the robotic arm's position and orientation using metrology equipment.
• For biocompatibility and electrical safety/EMC, the "ground truth" is adherence to the specified ISO/IEC consensus standards.

8. The sample size for the training set

• This information is not provided in the text. The CUVIS-spine is a robotic system, not explicitly an AI model trained on a dataset of images in the conventional sense for diagnostic AI. While it uses software, the text does not describe a training set for machine learning.

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

• As a training set is not explicitly mentioned for a machine learning model, this information is not applicable based on the provided text.

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