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EndoSize is a software solution that is intended to provide Physicians and Clinical Specialists with additional information to assist them in reading and interpreting DICOM CT scan images of structures of the heart and vessels.

EndoSize enables the user to visualize and measure (diameters, lengths, volumes, angles) structures of the heart and vessels.

EndoSize enables visualization and measurement of the heart and vessels for pre-operational planning and sizing for cardiovascular interventions and surgery, and for postoperative evaluation.

General functionalities are provided such as:

• Segmentation of cardiovascular structures
• Automatic and manual centerline detection
• Visualization of CT scan images in every planes, 2D review, 3D reconstruction, Volume Rendering, MPR, Streiched CMPR
• Measurement and annotation tools
• Reporting tools

EndoSize is a stand-alone software application that runs on any standard Windows or Mac OSX based computer which meets the minimal requirements. It enables Physicians and Clinical Specialists to select patient CT scan studies from various data sources, view them, and process the images thanks to a comprehensive set of tools. EndoSize is intended to provide a clinical decision support system during the preoperative planning of endovascular surgery.

EndoSize contains five modules dedicated to different types of endovascular interventions, EVAR, FEVAR, TEVAR, TAVI and Peripheral. These modules can be marketed in combination or as separate solutions. It is also possible to market custom versions of EndoSize to Stent manufacturers, based on the modules listed above. The differences between EndoSize and a custom version of EndoSize (user interface, manufacturer logo, manufacturer catalogue included in the software, of a generic module) do not modify neither the functioning nor the safety of the software.

EndoSize enables assessment and measurement of different vascular structures such as vessels, valves, aneurysms, and other anomalies. It provides simple techniques to assess the feasibility of endovascular procedures. EndoSize can combine 2D scan slices into comprehensive 3D models of the patient, and can display supporting DICOM CT scan data. The software accurately represents different types of tissue, making it easier to diagnose anomalies and plan interventional procedures. It works with DICOM CT scan images and can access multiple DICOM data files and PACS server.

The provided text describes the EndoSize software, its intended use, and its equivalence to a predicate device but does not contain detailed acceptance criteria or a specific study proving the device meets quantitative performance metrics. Instead, it refers to "bench tests" for validation.

Here's an attempt to answer your questions based only on the provided text, highlighting what is missing:

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

The document states: "Every specification of the EndoSize software is validated by a bench test before release."
It then lists the types of tests conducted:

• Tests of Importation of DICOM images
• Patient Manager tests
• Tests of image display and processing
• Functioning tests of the different modules EVAR, TEVAR, FEVAR, TAVI and Peripheral
• Measurement tests
• Reports creation and exportation tests

However, specific quantitative acceptance criteria (e.g., accuracy thresholds for measurements, speed requirements, specific segmentation performance metrics) and the numerical results of these tests (the "reported device performance") are NOT provided in this document. The document only states that the software "successfully undergone every bench testing designed to simulate clinical use."

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. It only mentions "bench tests" but does not detail the nature, size, or provenance of any testing datasets.

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. The document makes no mention of expert involvement in establishing ground truth for any test set. It does state that "The information and measurements displayed, exported or printed are validated and interpreted by Physicians," but this refers to end-user interpretation, not ground truth establishment for software validation.

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

This information is not provided.

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 provided. The document does not describe any MRMC studies or human reader performance evaluations.

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

The document refers to "bench tests" that validate "every specification of the EndoSize software." This implies a standalone performance evaluation of the software's functionalities. However, no specific quantitative results or methodology for such a standalone performance study are detailed. The listed tests (image importation, display, processing, module functioning, measurements, reports) suggest a system-level functional test, but not a rigorous clinical performance study.

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

This information is not provided. Given that the listed tests are functional ("importation," "display," "processing," "measurement," "reporting"), the ground truth for these might be internal reference standards or expected outputs rather than clinical data ground truth.

8. The sample size for the training set

This information is not provided. The document focuses on validation/testing and does not describe the development or training of any machine learning components, although features like "Automatic segmentation" and "Automatic centerline" suggest some underlying algorithms.

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

This information is not provided.

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