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Mirada XD is intended to be used by trained medical professionals including, but not limited to, radiologists, nuclear medicine physicians, and physicists.

Mirada XD is a software application intended to display and visualize 2D & 3D multi-modal medical image data. The user may process, render, review, store, print and distribute DICOM 3.0 compliant datasets within the system and/or across computer networks. Supported modalities include, CT, MR, PET, SPECT and planar NM. The user may also create, display, print, store and distribute reports resulting from interpretation of the datasets.

Mirada XD allows the user to register combinations of anatomical and functional images and display them with fused and non-fused displays to facilitate the comparison of image data by the user. The result of the registration operation can assist the user in assessing changes in image data, either within or between examinations and aims to help the user obtain a better understanding of the combined information that would otherwise have to be visually compared disjointedly.

Mirada XD provides a number of tools such as rulers and region of interests, which are intended to be used for the assessment of regions of an image to support a clinical workflow. Examples of such workflows include, but are not limited to, the evaluation of the presence or absence of lesions, determination of treatment response and follow-up.

Mirada XD allows the user to define, transform and export regions of interest structures in DICOM format including RT format for use in radiation therapy planning systems.

Mirada XD is a software application for displaying and visualizing 2D & 3D multi-modal medical image data such as CT, MR, PET, SPECT and planar NM. Mirada XD runs on a workstation with color monitor(s), keyboard, mouse and optional CD-RW. Mirada XD is designed to enable rendering, reviewing, storing, printing and distribution of DICOM 3.0 compliant datasets and reports within the system and/or across computer networks.

Mirada XD enables automatic and manual registration of combinations of anatomical and functional images that can be displayed with fused and non-fused displays to facilitate the comparison of image data by the user.

Mirada XD provides a number of tools such as rulers and region of interests through SUV calculation for the assessment of regions of an image to support a clinical workflow. Mirada XD also allows users to define, transform, store and export regions of interest structures in DICOM format including RTSS format for use in radiation therapy planning systems.

The provided text is a 510(k) summary for the Mirada XD device. It outlines the intended use, device description, and a general statement about testing. However, it does not contain specific acceptance criteria, detailed study results, or the other requested information (sample sizes, expert qualifications, adjudication methods, MRMC study details, standalone performance, ground truth types, or training set details).

The summary states: "Mirada XD is validated and verified against its user needs and intended use by the successful execution of planned performance, functional and algorithmic testing included in this submission. The results of performance, functional and algorithmic testing demonstrate that Mirada XD meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate devices."

This implies that detailed testing was performed and submitted to the FDA, but the summary itself only provides a high-level overview of the testing process and its conclusion regarding substantial equivalence to predicate devices. It specifically states "performance testing demonstrates that Mirada XD is substantially equivalent to, and performs at least as safely and effectively as the listed predicate devices," but it does not specify what those performance metrics or acceptance criteria were.

Therefore, for almost all the requested information, the answer is "Information not provided in the document."

Here is the breakdown based on the provided text:

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

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

• Sample Size for Test Set: Information not provided in the document.
• Data Provenance (Country of Origin, Retrospective/Prospective): Information not provided in the document.

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)

• Number of Experts: Information not provided in the document.
• Qualifications of Experts: Information not provided in the document.

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

• Adjudication Method: Information 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

• MRMC Comparative Effectiveness Study: The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. It focuses on the device's substantial equivalence to predicate devices, not on human-AI collaboration.
• Effect Size of Human Improvement with AI: Information not provided in the document.

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

• Standalone Performance Study: The document mentions "algorithmic testing" was performed, but does not provide details on whether this involved a standalone performance evaluation of the algorithms without human-in-the-loop, nor does it provide any results from such testing.

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

• Type of Ground Truth: Information not provided in the document. The document refers to "user needs and requirements," implying validation against operational criteria, but it does not specify a clinical "ground truth" for algorithmic performance.

8. The sample size for the training set

• Sample Size for Training Set: Information not provided in the document.

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

• Ground Truth Establishment for Training Set: Information not provided in the document.

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