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The VUZE System is intended to enable users to load pre-operative 3D images and planning data and register and overlay this data in real time with intra-operative 2D radiographic images of the same anatomy to support device guidance during interventional spinal procedures. The system also offers pre-operative surgical planning including implant sizing, entry location, and trajectory determination along with intra-operative guidance and tool trajectory / position confirmation.

The VUZE System (the "System") enables users to load 3D images and planning data then register and overlay this data in real time with intra-operative 2D radiographic images of the same anatomy. The System supports device quidance during minimally invasive spinal surgery, including the stabilization of the spine by means of fixation. fixation coupled with fusion, vertebroplasty or kyphoplasty. Applicable vertebrae are within the range of S1 through T7.

The System offers optional pre-operative surgical planning including implant sizing, entry location and trajectory determination, along with intra-operative guidance and tool trajectory/position confirmation by displaying a graphical representation of a tool tracked by intra-operative 2D images onto a patient's pre-operative 3D images.

The System's main components include:

• A workstation running the VUZE Planning and Procedure software (pre-installed)
• A housing for the workstation, with a front door for user access as well as a back service door
• A 32" touchscreen
• A mouse
• An isolation transformer
• An internal video acquisition device (frame grabber)
• A minimal-footprint, wheeled cart on which the above-listed items are placed.
• DVI cable with galvanic isolator
• An optional standalone planning station (planning software running on a commercial PC with identified specifications)
• Optional C-arm orientation sensors (3), charger, and associated Bluetooth dongle (Note: The orientation sensor may also be referred to under an acronym of IMU)
• Optional OTS foot pedal and associated Bluetooth dongle

Acceptance Criteria and Study for the VUZE System (K232976)

Based on the provided FDA 510(k) summary, the VUZE System is a medical imaging system for spinal interventions. The submission focuses on evaluating the substantial equivalence of a modified VUZE System to its predicate device (original VUZE System K210830). The performance data presented primarily assesses the quantitative accuracy of the system's core function: registering pre-operative 3D images with intra-operative 2D radiographic images to guide surgical tools.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are established as Root Mean Square (RMS) specification limits and quantile limits for various accuracy parameters. The reported device performance is based on testing at "Recommend Angles" and "Minimal Angle Difference" scenarios.

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

The document does not explicitly state the numerical sample size (e.g., number of cases or images) used for the quantitative accuracy testing. It mentions "all combinations of X-Ray machines and CT/CBCT data" were used.

Data Provenance: Not explicitly stated as "retrospective" or "prospective." The testing described is "Simulated Use / Quantitative Accuracy," suggesting it was conducted in a controlled environment as part of verification and validation, rather than directly from real-world patient data. The provenance of the CT/CBCT data and X-ray images used for these simulations (e.g., from a specific country or derived from anonymized patient data) is not specified.

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

The document does not provide information on the number of experts or their qualifications for establishing the ground truth for the test set. The ground truth (GT) is referred to in "Tip deviation from GT trajectory" and "Predicted tip deviation from GT trajectory," implying a reference standard was used, but details about its establishment are absent. Given the type of accuracy metrics (e.g., tip deviation, direction error), it's highly likely a physical phantom or controlled simulation with precisely known geometries served as the ground truth.

4. Adjudication Method for the Test Set

The document does not mention an adjudication method (like 2+1, 3+1, or none) for the test set. This type of adjudication is typically used for image interpretation tasks involving multiple human readers, which is not the primary focus of the performance data presented here (which is quantitative accuracy of registration and tool trajectory).

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided text. The performance data focuses on the standalone quantitative accuracy of the device's algorithmic functions rather than assessing the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, the performance data presented is a standalone (algorithm only) performance evaluation. The tables show the accuracy of the VUZE System in calculating direction error, tip deviation, predicted tip deviation, and depth error against a "GT trajectory" and specifications. This assesses the algorithmic output itself without specifying human interaction in the measurement of these specific performance metrics.

7. Type of Ground Truth Used

The type of ground truth used is implied to be a precisely defined geometric reference or simulated trajectory (e.g., from a phantom or highly accurate simulation model). The metrics "Tip deviation from GT trajectory" and "Depth error from GT" strongly suggest a known, exact reference point or path that the system's output is being compared against. It is not expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It describes "modifications to the CT to X-ray registration algorithm" and a "Deprecation of the previous ML algorithm... Same functionality is now done by a traditional algorithm." This implies that prior algorithms might have used training data, but the current submission's focus is on the modified algorithms and their direct performance. If the current critical algorithms are "traditional" (i.e., not machine learning based), then training data in the conventional sense might be less relevant for this submission, although development of such algorithms could involve extensive testing against diverse data.

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

Since the document does not specify a training set sample size or details about a machine learning algorithm's training, it does not provide information on how the ground truth for a training set was established.

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The VUZE System is intended to enable users to load pre-operative 3D images and planning data and register and overlay this data in real time with intra-operative 2D radiographic images of the same anatomy to support device guidance during interventional spinal procedures. The system also offers pre-operative surgical planning including implant sizing, entry location, and trajectory determination along with intra-operative guidance and tool trajectory / position confirmation.

The VUZE System (the "System") enables users to load pre-operative 3D images and planning data and register and overlay this data in real time with intra-operative 2D radiographic images of the same anatomy. The System supports device quidance during minimally invasive spinal surgery, including the stabilization of the spine by means of fixation, fixation coupled with fusion, vertebroplasty or kyphoplasty. Applicable vertebrae are within the range of S1 through T7.

The System offers pre-operative surgical planning including implant sizing, entry location and trajectory determination, along with intra-operative quidance and tool trajectory/position confirmation by displaying a graphical representation of a tool tracked by intraoperative 2D images onto a patient's pre-operative 3D images.

The main system components include:

• Workstation running the VUZE Planning and Procedure software (pre-installed)
• Housing for the workstation, with a front door for user access as well as a back service door
• 32" touchscreen
• Isolation transformer
• Internal video acquisition device (frame grabber)
• Wheeled cart on which the above-listed items are placed.

The VUZE System is a medical imaging system intended to enable users to load pre-operative 3D images and planning data, and then register and overlay this data in real time with intra-operative 2D radiographic images of the same anatomy. This supports device guidance during interventional spinal procedures, including pre-operative surgical planning and intra-operative guidance/tool trajectory confirmation.

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

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of numerical acceptance criteria for specific performance metrics (e.g., accuracy values with defined thresholds). Instead, it states that the device successfully completed all evaluations and testing to demonstrate substantial equivalence and that technological differences do not raise new or different questions of safety and effectiveness.

The "Performance Data" section indicates:

• The VUZE System was verified and validated in accordance with 21 CFR 820.30.
• The device successfully completed all of the evaluations and testing shown below (referring to a list of tests).
• The results of testing provide assurance that the device is as safe and effective as the predicate.

Given this, the acceptance criteria are implicitly tied to the successful completion of the listed tests and adherence to recognized consensus standards, ensuring the device is "as safe and effective as the predicate." The reported performance is that the device met these implicit criteria by successfully completing all tests.

Implicit Acceptance Criteria and Reported Performance Summary:

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

The document does not specify a sample size for a test set in terms of patient cases or images.
It states: "No animal or human clinical data were needed to demonstrate substantial equivalency."
The testing appears to be primarily based on engineering verification and validation activities, including "Simulated Use / Quantitative Accuracy" and "Qualitative Image Output Validation," rather than a clinical test set with patient data. Therefore, details about data provenance (country of origin, retrospective/prospective) are not applicable or provided.

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

Since no human clinical data or explicit "test set" in a clinical sense with ground truth established by experts is mentioned, this information is not provided in the document. The validation relied on engineering and simulated testing.

4. Adjudication Method for the Test Set:

As no clinical test set requiring expert ground truth or adjudication is described, an adjudication method is not applicable and not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

• No, an MRMC comparative effectiveness study was not done.
• The document explicitly states: "No animal or human clinical data were needed to demonstrate substantial equivalency."
• Consequently, there is no effect size reported for human readers improving with or without AI assistance, as this type of study was not performed.

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

The device itself is a "Medical Image Management and Processing System" that provides "device guidance" and "tool trajectory/position confirmation" by displaying data for a human user (surgeon). It is not described as a standalone diagnostic or automated detection algorithm in the typical sense.
The listed tests include "Software Verification / Validation at the unit, integration and system levels" and "Simulated Use / Quantitative Accuracy," which would involve evaluating the algorithm's performance. The "Simulated Use" likely evaluates the system's performance as intended with a human-in-the-loop, even if using simulated data.
The prompt asks "algorithm only without human-in-the-loop performance". The information suggests that the system's function is to aid humans, so a purely "standalone" evaluation that completely excludes the human role might not be fully representative of its intended use. However, the software validation and quantitative accuracy testing would assess the algorithm's underlying technical performance. The document does not explicitly describe a "standalone" study in contrast to a human-in-the-loop study, but rather a comprehensive set of engineering tests for the full system.

7. The Type of Ground Truth Used:

For tests like "Simulated Use / Quantitative Accuracy" and "Qualitative Image Output Validation," the ground truth would likely be established through:

• Known simulated scenarios/phantoms: For "Simulated Use / Quantitative Accuracy," the "ground truth" would be the precisely known positions or trajectories in a simulated environment or phantom model, against which the system's output is measured.
• Pre-defined specifications/expected outputs: For "Qualitative Image Output Validation" and other verification steps, the ground truth would be the expected display properties, co-registration accuracy, or functional behavior as defined in the product requirements.
• Predicate device performance: Since substantial equivalence is claimed, the performance of the predicate device (Innova Vision Applications K092639) implicitly serves as a benchmark for safety and effectiveness.

There is no mention of expert consensus, pathology, or outcomes data being used as ground truth, as no human clinical data was used.

8. The Sample Size for the Training Set:

The document does not mention a training set or its sample size. The VUZE System is described as using "3D imaging data of the patient's target anatomy and pre-operative surgical planning information registered against intraoperative 2D x-ray images." This suggests it's an image processing and guidance system rather than a machine learning model that requires a distinct "training set" in the context of deep learning, although underlying algorithms might have been developed using some data. However, for regulatory purposes, specific training set data is not provided.

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

Since no training set is described, information on how its ground truth was established is not provided.

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