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Visible Patient Suite is medical imaging software that is intended to provide trained medical professionals with tools to aid them in reading, interpreting, and treatment planning for both pediatric and adult patients. Visible Patient Suite accepts DICOM compliant medical images acquired from a variety of imaging devices, including CT, MR.

This product is not intended for use with or for the primary diagnostic interpretation of Mammography images.

The software provides several categories of tools. It includes basic imaging tools for general images, including 2D viewing, volume rendering and 3D volume viewing, orthogonal Multi-Planar Reconstructions (MPR), image fusion, surface rendering, measurements, reporting, storing, general image management and administration tools, etc.

It includes a basic image processing workflow and a custom Ul to segment anatomical structures, which are visible in the image data (bones, organs, vascular/ainway structures, etc.), including interactive segmentation tools, basic image filters, etc.

It also includes detection and labeling tools of organ segments (liver, lungs and kidneys), including path definition through vascular/airway, approximation of vascular/airway territories from tubular structures and interactive labeling.

The software is designed to be used by trained professionals (including physicians, surgeons and technicians) and is intended to assist the clinician who is solely responsible for making all final patient management decisions.

Visible Patient Suite is a software suite and includes three software components: Visible Patient Sender (VP Sender), Visible Patient Lab (VP Lab), and Visible Patient Planning (VP Planning). Visible Patient Lab is the main software component of Visible Patient Suite and includes all modules available in the software suite (except for the DICOM files anonymization module present in the Visible Patient Sender module).

a) Visible Patient Sender
Visible Patient Sender includes only modules dedicated to data management. The software is a simple tool to anonymize multidimensional digital images acquired from a variety of medical imaqinq modalities (DICOM images). There is no 3D data volume interpretation in this software.

b) Visible Patient Lab
Visible Patient Lab includes all Visible Patient Suite modules: data management (except for DICOM files anonymization module), data analysis and data processing. This software offers a flexible solution to help trained medical professionals with image processing knowledge (usually radiologists or radiologist technicians) in (1) the evaluation of patient's anatomy and pathology, and (2) in the creation of a 3D model of the patient's anatomy. This software proposes flexible workflow options: visualization of patient's anatomy and pathology from medical images; creation a 3D model of the patient's anatomical structures, organ segments and volumetric data; creation of an anatomical atlas (a colored image where each color represents a structure); and exports these medical data to be analyzed or reviewed later.

c) Visible Patient Planning
Visible Patient Planning includes modules dedicated to data management and data analysis, and simply contains a subset of the software modules present in Visible Patient Lab. This software offers a flexible visualization solution to help trained medical professionals (clinicians) in the evaluation of patient's anatomy and pathology to plan therapy or surgery.

The provided text does not contain detailed acceptance criteria and the comprehensive study results to populate all the requested information for acceptance criteria and device performance. The document is a 510(k) clearance letter and a summary, which typically focuses on substantial equivalence rather than a full performance study report.

However, based on the available information, here's what can be extracted:

Acceptance Criteria and Device Performance (Limited Information Available)

Study Details from the Document:

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated. The document mentions "phantom data with pre-established physical characteristics" for volume and distance measurements, and "all functionalities were tested." The exact number of phantom images or real patient cases used for testing is not detailed.
   • Data Provenance: Phantom data (for volume/distance). No specific country of origin for any clinical data is mentioned, nor is it specified if the testing was retrospective or prospective. The document states a "literature study was conducted to support device performance" during validation, suggesting external data may have been referenced, but details are absent.

2. 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 in the document.

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

   • This information is not provided in the document.

4. 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:

   • It is highly unlikely an MRMC study was done or reported here. The device is described as "Medical Image Management and Processing System" with tools for reading, interpreting, and treatment planning, along with segmentation and labeling. It explicitly states it is not for primary diagnostic interpretation of mammography images and is "intended to assist the clinician who is solely responsible for making all final patient management decisions." There is no mention of an AI-assistance study or any effect size on human reader improvement. The focus is on the device's capability to perform its specified image processing functions.

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

   • The description of testing on "phantom data with pre-established physical characteristics" for volume and distance computation suggests a form of standalone performance evaluation against a known ground truth. However, specific standalone performance metrics (e.g., precision, recall, Dice score for segmentation) are not reported beyond the general statement that "all functionalities were tested."

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

   • For volume computation and distance measurement, the ground truth was "pre-established physical characteristics" of phantom data.
   • For other segmentation and labeling functionalities, the type of ground truth used for testing is not explicitly stated. It can be inferred that it would likely involve expert-derived annotations if real patient data was used, but this is not confirmed.

7. The sample size for the training set:

   • This information is not provided as this document focuses on validation/testing.

8. How the ground truth for the training set was established:

   • This information is not provided as this document focuses on validation/testing.

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