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Keosys Medical Imaging Suite (KSWVWR) is intended to be used by trained medical professionals included, but not limited to, radiologists, nuclear medicine physicians and physicists.

Keosys Medical Imaging Suite is a software application intended to aid in diagnostic and evaluation of medical image data. Although this device allows the visualization of mammography images, it is not intended as a tool for primary diagnosis in mammography.

Keosys Medical Imaging Suite can be used for display, process, temporarily store, print and also create and print reports from 2D and 3D multimodal DICOM medical image data. The imaging data can be Computed Tomography (CT), Magnetic Resonance (MR), Radiography X (CR, DX, XRF, MG), Nuclear Medecine (NM) including planar imaging (Static, Whole body, Dynamic, Gated) and tomographic imaging (SPECT, Gated SPECT), Positon Emission Tomography (PT), Ultrasound (US).

Keosys Medical Imaging suite provides tools like rulers, markers or region of interests (e.g. it can be used in an oncology clinical workflow for tumor burden assessment or therapeutic response evaluation).

It is the user responsibility to check that the ambient luminosity conditions, the images compression ratio and the interpretation monitor specifications are consistent with a clinical diagnostic use of the data.

Keosys' Advanced Medical Imaging Software Suite (aka Viewer, aka KSWVWR) is a multimodality diagnostic workstation for visualization and 3D post-processing of Radiological and Nuclear Medicine medical images. It includes dedicated applications for the therapeutic response evaluation process in a multi-vendor, multi-modal and multi time-points context. The solution also includes the latest recommendations for SUV calculation.

Here's an analysis of the provided text to extract the acceptance criteria and details about the study, as requested.

Note: The provided document is a 510(k) summary for the "Advanced Medical Imaging Software Suite (KSWVWR)". It outlines the device's indications for use and compares it to predicate devices. However, it does not contain detailed acceptance criteria, specific study results, or information about sample sizes, ground truth establishment methods, or expert qualifications for a performance study. The document primarily focuses on demonstrating substantial equivalence to previously cleared devices rather than providing a standalone performance study report. Therefore, many of your requested points cannot be directly addressed from this text.

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document does not explicitly state quantitative acceptance criteria or detailed reported device performance in a study. The focus is on functionality and equivalence.

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

The document does not provide details on a specific "test set" with sample sizes or data provenance (e.g., country of origin, retrospective/prospective) for a performance study. The testing mentioned is part of the general software development process.

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

This information is not provided in the document.

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

The document does not describe an MRMC comparative effectiveness study where human readers used the AI. The device is a "viewer" and "software application intended to aid in diagnostic and evaluation." It provides tools but is not explicitly an "AI" in the sense of providing automated diagnostic suggestions or classifications to be compared with human performance with/without its assistance. It enables the display and manipulation of images and provides measurement tools.

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

The document describes the device as a "software application intended to aid in diagnostic and evaluation of medical image data" and that it "provides tools like rulers, markers or region of interests." It is a diagnostic workstation for visualization and 3D post-processing, and its use is by "trained medical professionals." This implies a human-in-the-loop device. There is no mention of a standalone algorithm performance study.

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

This information is not provided in the document.

8. The sample size for the training set

The document does not describe any machine learning or AI components that would historically require a "training set" in the context of supervised learning, nor does it mention a sample size for such.

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

Not applicable, as no training set is described.

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