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The Remi Robotic Navigation System is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of a tool holder or guide tube to be used by surgeons for navigating and/or guiding compatible surgical instruments in open or percutaneous spinal procedures in reference to rigid patient anatomy and fiducials that can be identified on a 3D imaging scan or 2D fluoroscopic images. The Remi Robotic Navigation System is indicated for assisting the surgeon in placing pedicle screws in the posterior lumbar region (L1-S1). The system is designed for lumbar pedicle screw placement with the prone position and is compatible with the Accelus LineSider Spinal System.

The Remi Robotic Navigation System (Remi) is an image guided system primarily comprised of a computer workstation, software, a trajectory system, including a targeting platform, a camera, and various image guided instruments intended for assisting the surgeon in placing screws in the pedicles of the lumbar spine. The system operates in a similar manner to other optical-based image y systems.

The provided text describes a 510(k) premarket notification for the Remi Robotic Navigation System (K223350). The submission focuses on demonstrating substantial equivalence to its predicate devices, particularly an earlier version of the Remi Robotic Navigation System (K223070) and the EXCELSIUS GPS (K171651). The key change in the subject device is the addition of compatibility with 2D fluoroscopic imaging systems for pedicle screw placement in the posterior lumbar region (L1-S1).

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for the tested device. It broadly states that "The testing shows that the use of the 2D fluoroscopic images with the Remi system is equivalent to the use of the validated 3D imaging systems." and that the device "continues to meet design requirements, is as safe and effective as the predicate device, and performs according to its intended use."

However, the "POSITIONING ACCURACY (BENCH)" for the Primary Predicate Device is listed as: 0.74 ± 0.36 mm (worst case); 95% CI: 1.46mm (worst case). Since the subject device "Same as Primary Predicate" for this characteristic and the testing was done to demonstrate equivalence, it can be inferred that the acceptance criterion for accuracy for the new functionality is to maintain this level of accuracy or be equivalent to it.

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

The document mentions "Performance Testing - Bench" was conducted, including "Navigation Accuracy Verification," "System Accuracy Validation," "Software System Test," "ASTM F2554 Accuracy Test," and "Software Unit and Integration Tests." However, specific sample sizes for these tests (e.g., number of cases, images, or measurements) are not provided.

The data provenance is not explicitly stated in terms of country of origin or whether it was retrospective or prospective. Given that it is bench testing, it is likely that the data was generated in a controlled laboratory or testing environment rather than being derived from patient cases.

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

This information is not provided. The document describes bench testing, which typically uses defined physical standards or simulated scenarios to establish ground truth rather than expert consensus on clinical images/data.

4. Adjudication Method for the Test Set

This information is not provided. Adjudication methods (like 2+1, 3+1) are typically used in studies involving human interpretation of clinical data and subsequent consensus determination. Since the testing described is bench testing, such an adjudication method is unlikely to apply.

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

No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence through bench testing, verifying that the added 2D fluoroscopy functionality maintains the device's accuracy and safety profiles compared to its predicate with 3D imaging. There is no information regarding human readers improving with or without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only) performance study was done. The performance testing described (Navigation Accuracy Verification, System Accuracy Validation, Software System Test, ASTM F2554 Accuracy Test, Software Unit and Integration Tests) are all characteristics of a standalone performance evaluation, focusing on the device's technical capabilities without human interaction determining performance outcomes within the tests themselves. The stated purpose was "to demonstrate that the updated requirement for this change was met and to ensure the risk profile of Remi was maintained," specifically concerning "the use of the 2D fluoroscopic images with the Remi system is equivalent to the use of the validated 3D imaging systems."

7. Type of Ground Truth Used

The ground truth for the bench testing would likely involve engineered physical phantoms, precisely manufactured test fixtures, and controlled experimental setups with known geometric parameters and validated measurements. For example, for "Navigation Accuracy Verification" and "System Accuracy Validation," physical measurements against a known standard or calibrated instruments would establish the ground truth. For "Software System Test" and "Software Unit and Integration Tests," the ground truth would be defined by the software's specified functional requirements and expected outputs.

8. Sample Size for the Training Set

The document does not provide information about a training set or its sample size. The submission is for a modification to an existing cleared device, specifically adding 2D fluoroscopic image compatibility. While the software was updated to support this, including an algorithm correcting distortion, the text implies that the evaluation focused on the performance of the system with the new capability rather than the development of a completely new AI algorithm requiring extensive training data. If machine learning was used for the distortion correction, details about its training would typically be in a separate section not provided here.

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

As no training set is explicitly mentioned, this information is not provided.

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The REMI™ Robotic Navigation System is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of a tool holder or guide tube to be used by surgeons for navigating and/or guiding compatible surgical instruments in open or percutaneous spinal procedures in reference to rigid patient anatomy and fiducials that can be identified on a 3D imaging scan. The REMI™ Robotic Navigation System is indicated for assisting the surgeon in placing pedicle screws in the posterior lumbar region (LI-S1). The system is designed for lumbar pedicle screw placement with the prone position and is compatible with the Accelus LineSider® Spinal System.

The Remi Robotic Navigation System (Remi) is an image guided system primarily comprised of a computer workstation, software, a trajectory system, including a targeting platform, a camera, and various image guided instruments intended for assisting the surgeon in placing screws in the pedicles of the lumbar spine. The system operates in a similar manner to other optical-based image y systems.

The provided text outlines the FDA 510(k) clearance for the REMI Robotic Navigation System, focusing on its substantial equivalence to a predicate device. However, it does not contain a detailed study report or explicit acceptance criteria with reported device performance metrics in the format requested.

The document primarily focuses on demonstrating that the updated REMI system, with additional compatible 3D imaging systems, is substantially equivalent to its predicate. The "Performance Testing - Bench" section mentions tests conducted but does not provide specific numerical acceptance criteria or performance results.

Therefore, much of the requested information cannot be extracted directly from the provided text. I will indicate where information is missing or inferred.

Acceptance Criteria and Device Performance

Study Details from the provided text:

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

   • The document mentions "Performance Testing - Bench" and "Verification and validation testing" but does not specify the sample size for any test set or the data provenance (e.g., country of origin, retrospective/prospective). It suggests bench testing was primarily used for equivalence.

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

   • No information provided. The "performance testing" described appears to be technical validation against specified equivalence factors rather than expert review of clinical outcomes or images.

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

   • No information provided.

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 described. This device is a robotic navigation system for spinal surgery, not an AI-assisted diagnostic imaging interpretation tool that would typically involve human readers. Its purpose is to aid surgeons in pedicle screw placement.

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

   • The document does not explicitly describe a "standalone" algorithmic performance test in the context of an AI-only system. The device is a navigation system that guides a human surgeon. Its performance metrics, like accuracy, are inherently tied to the system's ability to guide to a planned trajectory, which can be measured quantitatively in bench tests. The bench testing mentioned covers aspects like "Accuracy," "Image Quality," "Image Transfer Speed," and "Image Registration Speed."

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

   • Based on the description of "Performance Testing - Bench" and "Accuracy verification on anatomical landmarks" (for the predicate), the ground truth for accuracy testing would typically involve precisely measured physical points or targets on a phantom or model, measured by a highly accurate reference system (e.g., CMM). For image quality, transfer, and registration speed, the ground truth would be objectively defined technical specifications or measurements.

7. The sample size for the training set:

   • No information provided about a "training set." The REMI system is a robotic navigation system, not described as a deep learning or machine learning-based algorithm that typically requires a large training dataset for model development. The system uses pre-programmed logic, image processing, and control algorithms.

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

   • Not applicable, as no training set for an AI/ML model is mentioned.

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