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MIGUE is intended to aid in patient target to radiation beam set-up for administration of radiation therapy. It is indicated for all body procedures.

MIGUE is intended to be used to place patients at the isocenter of a linear accelerator for stereotactic radiosurgery or radiotherapy procedures. MIGUE uses x-ray registration as the method of locating the position of the patient. It is indicated for all body procedures.

The device consists of three units:

1. MIGUE Main Unit incorporates two angularly spaced identical Fluoroscopic Channels, mounted on a rigid ring, for acquisition of X-ray images.
2. Console Workstation PC with embedded software, used for control, display and image processing.
3. Electronics Cabinet.

Principle of operation:
a) Acquire two images, one from each channel.
b) Each channel displays an acquired image and a marker indicating the IsoCenter projection onto the fluoroscopic screen.

The provided text does not contain detailed information regarding the acceptance criteria, study design, or performance data for the MIGUE device. The document is a 510(k) summary and an FDA clearance letter, which primarily focuses on the device's substantial equivalence to predicate devices and its indications for use.

Therefore, many of the requested details cannot be extracted from the given input.

Here's a breakdown of what can be inferred or stated from the provided text, and what cannot:

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

This information is not present in the provided text. The document states "The Substantial Equivalence table provides a comparison of MIGUE's technological characteristics to those of the predicate devices. The table is located in Section 8 of this submission." However, Section 8 is not included in the provided snippets. Without this section, specific acceptance criteria or performance metrics are unknown.

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

This information is not present in the provided text. The document does not describe any specific clinical or performance studies, test sets, or data provenance.

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 present in the provided text. There is no mention of ground truth establishment or expert involvement in any testing.

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

This information is not present in the provided text. No adjudication method is described.

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 information is not present in the provided text. The MIGUE device, as described, is an X-ray registration system for patient placement, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance would not be applicable, and no such study is mentioned.

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

This information is not explicitly stated as a formal "standalone study." However, the description of MIGUE indicates its primary function is automated X-ray registration for patient positioning. Its "Principle of operation" describes acquiring images, displaying them, and indicating the isocenter projection. This implies an inherent standalone algorithmic function to perform its intended task, operating without direct human intervention in the core image analysis for positioning (though human oversight and control are certainly part of the workflow). The document doesn't detail a specific study proving this standalone performance, but the device's function implies it.

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

This information is not present in the provided text.

8. The sample size for the training set

This information is not present in the provided text. There is no mention of a "training set," implying that if machine learning is involved, it's not discussed in this summary, or the device relies on traditional image processing/registration algorithms rather than learned models requiring a training set.

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

This information is not present in the provided text, as no training set or ground truth for it is mentioned.

In summary, the provided document is a regulatory submission summary (510(k)) focusing on equivalence to predicate devices and indications for use, rather than a detailed technical or clinical study report. Therefore, most of the requested performance and study details are not available within the given text.

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