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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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The AIRO® is intended to be used for X-ray computed tomography applications for anatomy that can be imaged in the 107cm aperture excluding patients weighing over 400 lbs (182 kg).

The Mobius Airo is a Mobile Intraoperative Computed Tomography (CT) System. The Airo has a large-diameter bore designed for intraoperative use; the main features include a 107cm bore, with a 51.2cm field of view (FOV). The Airo has two modes of operation; transport and scanning (both helical and axial). In its scanning mode, translation along the longitudinal axis is achieved through movement of the gantry along the length of the system base (rather than through movement of the patient support table).

The lightweight translating gantry consists of a rotating disk with a solid-state X-ray generator, solid state detector array (that includes detector modules that consist of Gadolinium Oxysulfide (GOS) and Photodiode Array). Each detector module includes a 32 x 16-pixel scintillator array that produces scintillation events responsive to irradiation by X-rays. The Airo also includes a collimator, control computer, communications link, data acquisition system, reconstruction computer, power system, brushless DC servo drive system (disk rotation), and a DC brushless servo drive system (translation).

The power system consists of batteries which provide system power while unplugged from a standard power outlet (e.g., during transport of the System and also during scanning). The base has retractable rotating caster wheels and electrical drive system can be easily moved to different locations.

In addition, the System has the necessary safety features such as emergency stop button, X-ray indicators, interlocks, patient alignment lasers, and 110 percent X-ray timer. The software helical and axial reconstruction algorithms are both based on an exact filtered-back projection.

The provided text describes a 510(k) premarket notification for the AIRO® Computed Tomography (CT) X-ray System (K180393). This submission aims to demonstrate substantial equivalence to a legally marketed predicate device (K160126) and primarily concerns the addition of pediatric scanning features/protocols and the removal of pediatric restrictions from the Indications for Use. The study conducted to meet acceptance criteria is a series of non-clinical bench tests and technical verification and validation activities rather than a clinical trial involving human subjects or AI-specific performance evaluation in a diagnostic context.

Here's an analysis of the provided information:

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

The document does not explicitly present a table of acceptance criteria for specific performance metrics alongside reported device performance. Instead, it states that the modified device's performance, particularly related to the added pediatric features and protocols, meets acceptance criteria through various testing. The comparison table (pages 5-7) primarily highlights the equivalence of technological characteristics between the proposed device and the predicate. The key differences causing the need for this submission are:

• Added pediatric scanning feature/protocols: This required the addition of 80 and 100 kV scanning capabilities and updated software to include age and height parameters.
• Modified Indications for Use Statement: Removal of pediatric restriction.

The "reported device performance" is a general statement that "the modified Airo CT System meets the acceptance criteria" based on the performed non-clinical tests. Specific numerical performance metrics (e.g., image quality scores, dose reduction percentages for pediatric protocols) are not detailed within the provided text. The only specific metric given is "Spatial Resolution for Sharpest Clinical Algorithm (Ip/cm at 2%) = 6.9," which is identical to the predicate.

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

The study described is a non-clinical bench testing and verification/validation effort. Therefore, there is no "test set" in the sense of a dataset of patient images or clinical cases. The "test set" would consist of phantoms used for image quality metrics, dose testing, and protocol validation. The text mentions:

• "Battery of bench testing with phantom images presented in Section 18." (Section 18 is not included in the provided text).
• "Image Quality Metrics and phantom images for Pediatrics."
• "Pediatrics Protocol Design & Validation (using Image Gently, ACR and AAPM guidelines)."
• "Radiation/Dose Testing."

The provenance of this phantom data would be internal testing conducted by Mobius Imaging, LLC in the USA. It is inherently "prospective" in the sense that the tests were designed and executed to validate the modified device.

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

Given that this is a non-clinical bench study focused on engineering validation and demonstrating general safety and effectiveness, there is no mention of experts establishing a "ground truth" for a test set in the diagnostic interpretation sense. The "ground truth" for performance would be derived from physical measurements on phantoms and compliance with recognized standards and guidelines (e.g., Image Gently, ACR, AAPM guidelines for pediatric protocols). While experts might have been involved in defining these standards or interpreting test results, their number and specific qualifications are not specified here.

4. Adjudication method for the test set

Not applicable, as this is a non-clinical engineering study of a CT device itself, not an AI software evaluating images, and therefore does not involve human adjudication of diagnostic interpretations.

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. This submission is for a CT X-ray system, not an AI-powered diagnostic tool. Therefore, an MRMC comparative effectiveness study to assess human reader improvement with or without AI assistance was not performed or described.

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

No. The AIRO® Computed Tomography (CT) X-ray System is a medical imaging device, not a standalone AI algorithm for image analysis. The "AI" in AIRO® is likely part of the product name and not an indicator of artificial intelligence functions for diagnosis or analysis in the context of this submission. The "software" updates mentioned are for scanner control and protocol management, not diagnostic AI.

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

For the non-clinical testing conducted, the "ground truth" primarily refers to established physical standards and metrics for CT image quality, radiation dose, and compliance with recognized regulatory and industry standards (e.g., IEC 60601 series, NEMA XR standards, Image Gently, ACR, AAPM guidelines). This "ground truth" is based on:

• Physical measurements on phantoms: To assess image quality, spatial resolution, contrast, noise, and radiation dose.
• Compliance with specified engineering and performance requirements: As defined by the company and aligned with international and national standards.

8. The sample size for the training set

Not applicable. This is a submission for a CT hardware system with software updates, not an AI model requiring a training set of data.

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

Not applicable, as there is no training set for an AI model in this submission.

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