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Imaging of:

• The Whole Body (including head, abdomen, breast, pelvis, limbs and extremities, spine, . neck, TMJ, heart, blood vessels). [Application terms include MRCP (MR neok, Ywis, Hoard Broomy), MR Urography, MR Myelography, MR Fluoroscopy, SAS (Surface Anatomy Scan), Dynamic Scan and Cine Imaging.]
• Fluid Visualization ●
• 2D/3D Imaging .
• Mr Angiography/MR Vascular Imaging .

Version 2.5/V2.6 software is a combination of modifications and the enhancement of sequences to the existing software, which facilitate the acquisition and reconstruction of MR images. This software will function on both SGI Indigo 2 and O2 computer workstations. The V2.6 software may also be identified as Performance Plus option.

The provided document is a 510(k) Premarket Notification for a diagnostic magnetic resonance device (OPART™ version 2.5/2.6 software for MRT-600) from 1999. It focuses on the safety and imaging performance parameters of the MR system and its software updates. It does not contain information related to a clinical study with acceptance criteria for a device's diagnostic performance, nor does it describe a study involving human readers, ground truth establishment, or multi-reader multi-case analysis as typically seen for AI/CAD devices.

Therefore, most of the requested information cannot be extracted from the provided text.

Here is what can be inferred or stated based on the document:

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

The document lists "Safety Parameters" and "Imaging Performance Parameters" which function as specifications for the device. These are accepted as meeting the requirements for substantial equivalence.

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 provided in the document. The document mentions "sample phantom images and clinical images" were presented, but no details on the sample size, provenance, or study design are given.

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 document. The evaluation of imaging performance relies on "consensus standards requirements," implying expert review or established metrics, but specifics on the number or qualifications of experts are absent.

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

This information is not provided in the document.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or at least not described. This document pertains to the software update of an MR imaging system itself, not an AI or CAD system designed to assist human readers in interpretation.

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

This document describes the performance of the MR imaging system and its software, which acquires and reconstructs images. Its performance is inherently "standalone" in terms of image generation, but it's not an algorithm that performs diagnostic interpretation without a human-in-the-loop. It confirms the physical and imaging quality of the system, not a diagnostic algorithm.

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

For the "Imaging Performance Parameters," the "ground truth" or reference standard appears to be consensus standards requirements for objective metrics like Signal-to-Noise ratio, Uniformity, Slice Profiles, Geometric Distortion, and Slice Thickness/Interslice Spacing. This is based on physical or technical measurements and comparisons, rather than clinical ground truth like pathology or outcome data.

8. The sample size for the training set

This information is not provided in the document. The software update (version 2.5/2.6) enhances sequences and does not imply a "training set" in the context of machine learning, which wasn't prevalent for such regulatory submissions in 1999.

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

This information is not provided in the document and is not applicable in the context of this type of device and submission from 1999.

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