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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 pediatric patients and 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 patient mode). 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 a layered Cadmium Tungstate (CdWO4) 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 the Mobius Imaging AIRO® Computed Tomography (CT) X-ray System and its substantial equivalence to a predicate device (Airo Mobile CT System – K131431). However, the document does not contain explicit acceptance criteria and a detailed study proving the device meets those criteria in a format that directly addresses all the requested information categories.

Instead, it focuses on demonstrating substantial equivalence through a comparison of technological characteristics and a summary of non-clinical tests.

Here's an attempt to extract and infer the requested information based on the provided text, while also noting what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of acceptance criteria with specific performance metrics and their corresponding reported device performance values. The closest information is the comparison table of technological characteristics to the predicate device, implying that equivalence to the predicate's performance serves as an acceptance criterion for these features.

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

The document lists "Image Phantom Data," "Historical Clinical Image Data Reconstruction Comparison," and "Offline Reconstruction Clinical Image Comparison (for NMAR feature)" as non-clinical tests. However, it does not specify the sample size for these test sets, nor the country of origin or retrospective/prospective nature of any clinical image data used.

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

The document mentions "Independent Review Analysis for Diagnostic Image Quality" but does not provide details on the number of experts, their qualifications, or how they established ground truth.

4. Adjudication method for the test set

The document does not describe any adjudication method used for establishing ground truth for the test set.

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 document describes a CT X-ray system, not an AI-assisted diagnostic tool that would typically involve human reader improvement. Therefore, no MRMC comparative effectiveness study was done in the context of human reader improvement with AI.

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

The document specifies "Image Quality Metrics," "Image Phantom Data," and "Historical Clinical Image Data Reconstruction Comparison" as tests. These tests assess the device's technical performance and image quality, which are indicative of standalone (algorithm only) performance. However, specific metrics and results are not provided. The phrase "Offline Reconstruction Clinical Image Comparison (for NMAR feature)" also suggests standalone evaluation of the algorithm.

7. The type of ground truth used

For objective metrics like spatial resolution, phantom data likely served as ground truth. For "Historical Clinical Image Data Reconstruction Comparison" and "Offline Reconstruction Clinical Image Comparison," the ground truth would likely be the original clinical images from the predicate device (K131431) or accepted diagnostic standards derived from them, to ensure the modified device's images are comparable. The document also mentions "Independent Review Analysis for Diagnostic Image Quality," which suggests expert review/consensus was involved in parts of the evaluation.

8. The sample size for the training set

This document primarily describes the substantial equivalence of a medical imaging device (CT scanner). While it mentions reconstruction algorithms and a Metal Artifact Reduction (MAR) algorithm, it does not provide information on a "training set" as would be typical for machine learning-based AI devices. The focus is on the device's hardware and software performance characteristics in comparison to a predicate.

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

As no training set is explicitly mentioned or detailed for an AI component in the conventional sense, this information is not applicable/provided. The ground truth relevant to the device's performance evaluation would be related to its imaging capabilities, not the training of a diagnostic AI model.

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