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The O-arm O2 Imaging System is a mobile x-ray system designed for adults and pediatric patients weighing 601bs or greater and having an abdominal thickness greater than 16cm, and is intended to be used where a physician benefits from 2D and 3D information of anatomical structures and objects with high x-ray attenuation such as bony anatomy and metallic objects.

The O-arm O2 Imaging System in compatible with certain image guided surgery systems.

The O-arm™ O2 Imaging System is a mobile x-ray system that provides 3D and 2D imaging. O-arm O2™ Imaging System was originally cleared for market under the original 510(k) K151000 and subsequently via special 510(k) K173664. The device is classified under primary product code OWB (secondary product codes OXO, JAA) ref 21 CFR 892.1650.

This submission for the O-arm™ O2 Imaging System 4.2.0 software release introduces following features:

• . "2D Long Film" Imaging Protocol (Intraoperative Radiographic Scan)
• . Gantry Rotor Angle and Tilt Angle Display
• User Access Enhancements ●

The O-arm™ O2 Imaging System consists of two main assemblies that are used together:

• The Image Acquisition System (IAS) ●
• The Mobile View Station (MVS) .

The two units are interconnected by a single cable that provides power and signal data. The IAS has an internal battery pack that provides power for motorized transportation and gantry positioning. In addition, the battery pack is used to power the X-ray tank. The MVS has an internal UPS to support its function when mains power is disconnected.

The O-arm™ O2 Imaging System operates off standard line voltage within the following voltages:

• VAC 100, 120 or 240 ●
• Frequency 60Hz or 50Hz ●
• Power Requirements 1440 VA

The provided text describes a 510(k) premarket notification for the Medtronic O-arm™ O2 Imaging System 4.2.0 software. This submission aims to demonstrate substantial equivalence to a predicate device (O-arm™ O2 Imaging System 4.1.0 software).

The key takeaway is that this submission modifies an existing imaging device, adding new features, rather than being a new AI/ML-driven diagnostic/CADe device that would typically have specific AI-driven acceptance criteria. Therefore, the "acceptance criteria" and "study" described in the document are primarily focused on demonstrating that the modified device remains safe and effective and performs as intended, similar to the predicate device, for its original intended use. There is no mention of AI/ML components in this submission.

Given this context, I will address the questions as they relate to the information provided about the device's modifications and the performance testing conducted to support its substantial equivalence.

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

Based on the provided document, the acceptance criteria are implicitly tied to demonstrating substantial equivalence to the predicate device and showing that the new features function as intended without introducing new risks. The "reported device performance" is described through various testing types.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify exact sample sizes for all tests beyond general statements like "Usability Testing was conducted" or "The Image Quality Assessment... provides a quantitative image quality assessment."

The "O-arm™ Cadayer Image Pair Study" is the most relevant test mentioned for clinical utility:

• Sample Size: Not explicitly stated in the provided text.
• Data Provenance: Not explicitly stated (e.g., country of origin). It's likely a controlled, prospective study given the nature of a "Cadayer Image Pair Study" for comparing device versions. However, the specific type (retrospective vs. prospective) is not detailed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the excerpt. For the "O-arm™ Cadayer Image Pair Study," it's highly probable that medical professionals (e.g., radiologists, surgeons) were involved in evaluating image utility, but their number and specific qualifications are not mentioned.

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

The document does not specify any adjudication methods for the "O-arm™ Cadayer Image Pair Study" or other performance tests.

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

• MRMC Study: The "O-arm™ Cadayer Image Pair Study" mentioned sounds like it could potentially involve multiple readers evaluating images. However, the document does not explicitly state that it was an MRMC study, nor does it describe its design in detail.
• AI Assistance: This device and the listed modifications do not involve AI/ML. The effect size of human readers improving with AI assistance is therefore not applicable and not discussed. The study's purpose was to compare the image utility of the new software version to the predicate software version of the imaging system itself.

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

• Standalone Performance: This question is typically asked for AI/ML algorithms. Since the O-arm™ O2 Imaging System 4.2.0 software does not contain an AI/ML algorithm within the scope of this 510(k) submission, this concept is not applicable. The device is an imaging system, and its performance is evaluated as a system (hardware and software combined), not as a standalone algorithm. The "2D Long Film" feature is described as an automated stitching process which is a traditional image processing function, not an AI/ML algorithm requiring separate "standalone" validation.

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

For the "O-arm™ Cadayer Image Pair Study," the "ground truth" would likely be the clinical utility or perceptual quality of the images as evaluated by medical professionals. This would closer resemble expert opinion/consensus on image quality and usefulness in a clinical context, rather than a definitive "true positive/negative" based on pathology or outcomes data, as it's a comparison of imaging system versions. However, the document doesn't explicitly define what "ground truth" means for this specific study.

8. The sample size for the training set

This device does not appear to utilize AI/ML algorithms that would necessitate a "training set." Therefore, this question is not applicable.

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

As there is no mention of a "training set" for AI/ML, this question is not applicable.

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