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Cercare Medical Neurosute and associated modules, including the Capillary Function module, is an image processing software package to be used by trained professionals, including physicians and medical technicians.

The software package runs on standard off-the-shelf computer or a virtual platform, such as VMware, and can be used to perform image viewing, processing, and analysis of images. Data and images are acquired through DICOM (Digital Imaging and Communications in Medicine) compliant imaging devices

Cercare Medical Neurosuite provides viewing capabilities, whereas the Capillary Function module provides analysis capabilities for functional and dynamic imaging datasets acquired with MRI including a Diffusion Weighted MRI (DWI) Module and a Dynamic Analysis Module (dynamic contrast-enhanced imaging data for MRI).

The Capillary Function module is used for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to tissue flow (perfusion) and tissue blood volume. In addition, the Capillary Function module's DWI technology is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data.

The Virtual Expert module allows the calculation of regions of interest and the visual inspection of time attenuation curves. One clinical application is to visualize the apparent blood perfusion and to calculate ADC threshold volume, Tmax threshold volume, and Mismatch Ratio in the brain tissue affected by acute stroke.

Areas of decreased perfusion appear as areas of changed signal intensity:

• · Lower signal intensity for CBF and CBV
• · Higher signal intensity for TTP, MTT, and Tmax

Cercare Medical Neurosuite is a software-only device designed to streamline medical image processing by providing for the visualization and study of medical images. CMN can be installed on a customer PC or it can be accessed remotely using remote desktop technologies. CMN provides viewing, quantification, analysis and reporting capabilities. CMN is not intended as a dedicated PACS system for long term persistent storage of patient data.

CMN is software that is intended for use by trained professionals, including physicians and medical technicians. The software provides cerebral image processing capabilities. CMN is intended to be used as decision support software only and the clinician continues to provide all treatment decisions.

The software is intended to visualize and study neuroimaging by image viewing and registration of medical images. CMN works with MRI (Magnetic Resonance Image) technology.

CMN accepts and produces data sets in the DICOM format. DICOM is a standard format for storing and transmitting medical image data in vendor neutral format and is managed by the DICOM Standards Committee.

CMN is a platform that allows for the addition of certain modules for further analysis. One of these modules included in this submission is Capillary Function.

Capillary Function, when activated in the installed Cercare Medical Neurosuite, provides further functionalities for reading, writing, visualizing and studying medical images.

Capillary Function provides perfusion post-processing technologies, where dynamically acquired perfusion MRI series can be processed to yield information relevant for assessment of the hemodynamic status of a patient.

Capillary Function generates hemodynamic markers, which can be used for management of diseases with possibly compromised hemodynamic function, such as ischemic stroke and tumors.

The generated output maps can be viewed by standard DICOM image viewers. In addition. Capillary Function includes the possibility for post-processing diffusion-weighted imaging (DWI) MRI data. Post-processing of DWI data results in maps reflective of local water diffusion properties. The post-processed DWI-derived maps can be viewed in standard DICOM image viewers. Capillary Function thus works with MRI technology.

Virtual Expert, when activated in the installed CMN Capillary Function, provides further functionalities for reading, writing, visualizing, and studying medical images.

Virtual Expert provides automatic delineation of regions of interest (ROI) relevant for stroke patient assessment based on perfusion and diffusion image output generated by the Capillary Function module. Specifically, diffusion MRI images are used to generate threshold masks of perceived core lesions, whereas perfusion MRI images are used to generate threshold masks of perceived perfusion restriction. Virtual Expert thus works with MRI technology.

The generated masks can be combined into a mismatch region of interest.

Volumetric calculations and ratios can be calculated from the computed regions of interest.

Here's a breakdown of the acceptance criteria and study information for the Cercare Medical Neurosuite (CMN) Capillary Function with Virtual Expert, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Capillary Function Module:

  • Test Set: Predominantly digital phantoms. The text mentions "human-like phantom testing."
  • Data Provenance: Not explicitly stated for retrospective clinical data, but the phantoms are simulated.
  • Note: Retrospective clinical data was primarily used for visual inspection, not rigorous quantitative testing in this module's validation.

• Virtual Expert Module:

  • Test Set: Retrospective patient MR perfusion and diffusion imaging.
  • Data Provenance: Not explicitly stated (e.g., country of origin). The data is described as "retrospective clinical data."

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

• The document does not explicitly state the number of experts used or their specific qualifications for establishing ground truth for either module's test sets.
• For the Virtual Expert module, it mentions "comparison testing...in the associated published technical comparison study (Bathla et al, J. Neurointerventional Surg., 12:1028-1032 (2020))." This external publication might contain more details on expert involvement, but it's not present in this document.

4. Adjudication Method for the Test Set:

• The document does not explicitly state the adjudication method used for either module's test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its Effect Size:

• No, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not explicitly described in this document. The performance testing focused on the device's accuracy and agreement with ground truth or a predicate device, not on human reader improvement.
• The Virtual Expert module's secondary application assessment mentioned "image-driven decision to treat analysis through the so-called DEFUSE3 criteria," which hints at clinical relevance, but it's not a formal MRMC study of human performance improvement.

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

• Yes, standalone performance testing was conducted. The entire performance data section describes the testing of the CMN Capillary Function with Virtual Expert as a software-only device. The device was compared against known parameters (digital phantoms) and retrospective clinical data (yielding volumetric and spatial agreement) without explicit mention of human-in-the-loop performance during these validation steps.

7. The Type of Ground Truth Used:

• Capillary Function Module:
  • Digital Phantoms: "True parameter combinations were known." This means the ground truth was synthetically generated and precisely defined.
• Virtual Expert Module:
  • Retrospective Clinical Data: Ground truth implies a reference standard or consensus derived from the clinical images themselves for volumetric and spatial agreement. The comparison was modeled after a published technical study, which may provide insight into how ground truth was typically established in such contexts (likely expert consensus or a validated reference method).

8. The Sample Size for the Training Set:

• The document does not explicitly state the sample size used for the training set.

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

• The document does not explicitly state how the ground truth for the training set was established. In fact, it doesn't mention training data at all. The description focuses on validation and verification rather than model development.

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