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The PROcedure Rehearsal Studio software is intended for use as a software interface and image segmentation system for the transfer of imaging information from a medical scanner to an output file. It is also intended as pre-operative software for surgical planning

The PROcedure Rehearsal Studio software allows clinicians to create a patient specific 3D anatomical model based on a patient's CT for the purpose of preoperative surgical planning.

The 3D segmentation model produced by the PROcedure Rehearsal Studio may be exported to the Simbionix ANGIO Mentor Simulator Practice Environment and allow the physician to create a library of modules for training and post-operative debriefing. The ANGIO Mentor Simulator Practice Environment is not meant for clinical purposes and is intended to be used for training purposes only.

The modifications subject to this Special 510(k) submission are: (1) Graphic User Interface changes in various locations; (2) functional change in various locations which enable the addition of a Neuro module that allows the software to create 3D models for neurological scans in addition to the thoracic (TEVAR), abdominal (EVAR) and Carotid options that were previously cleared.

The Neuro module builds upon the previously cleared PROcedure Rehearsal Studio modules by adding the support for segmentation of the cerebral circulation system in addition to existing carotid, thoracic, and abdominal anatomies.

This document describes the PROcedure Rehearsal Studio, a software intended for use as a software interface and image segmentation system for surgical planning. The information provided primarily focuses on the device's modification to include a "Neuro module" and its substantial equivalence to previously cleared versions.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a table format with corresponding performance results. Instead, it states that all verification and validation tests "passed successfully." The testing activities covered "Importing Patient Data, Segmentation and Centerlines" and included "Segmentation quality testing," "Phantom testing," and "Usability testing."

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Four patient datasets were used for verification:
  • Dataset A: Carotid Type
  • Dataset B: EVAR type (Endovascular Aortic Repair)
  • Dataset C: TEVAR type (Thoracic Endovascular Aortic Repair)
  • Dataset D: Neuro Intervention type
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document. The text does not mention the use of experts to establish a ground truth for the test datasets or their qualifications.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

This information is not provided in the document. There is no mention of an MRMC study or an effect size for human readers with or without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The document describes "Software Verification and Validation Testing" that included "Segmentation quality testing" and "Phantom testing." While these tests likely assess the algorithm's performance in isolation, the document does not explicitly state that a standalone performance study was conducted to evaluate the algorithm's diagnostic or clinical performance without human interaction. The device is described as "software for surgical planning," implying human involvement in the planning process.

7. Type of Ground Truth Used

The document does not explicitly define the type of ground truth used for "Segmentation quality testing" or other verification activities. It mentions using "patient datasets" and "phantom testing," but the method for establishing the true segmentation for comparison is not detailed (e.g., expert consensus, pathology, or direct measurement). Given the nature of a 3D anatomical model from CT scans for surgical planning, a likely ground truth might involve expert-annotated segmentations or comparisons against highly precise imaging, but this is not stated.

8. Sample Size for the Training Set

This information is not provided in the document. The document focuses on verification using
four patient datasets, not on the training data used to develop the segmentation algorithms.

9. How the Ground Truth for the Training Set Was Established

This information is not provided in the document. As the training set size and details are absent, so is the method for establishing its ground truth.

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