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VM PACS with VM Medical Workstation is a software designed to receive, store, distribute, process and display Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data throughout a clinical environment. The software performs digital communication and storage of the images and is meant to be administrated by a trained user.

VM Medical Workstation and VM Clinical Viewer are both softwares capable to perform image processing, measurement and other diagnostics tools, it is designed to be used by trained professionals, including but not limited to physicians, nurses and medical technicians.

VM PACS is designed to Store Medical Dicom Images and leave them available to be used for diagnostics or transferred to other Dicom devices.

VM PACS is not meant to create or modify DICOM images. VM PACS store the images and the Data Set send by an DICOM compatible image diagnostic equipment and store them making them available to be access by a workstation or viewer, or been transfer to another Dicom Compatible device.

VM PACS does not modify the images store in it.

User Graphic interface VM Medical Workstation is a complete Workstation which provides diagnostic tools for image diagnose, for CT, MR, PT, CR, DX, NM, MG, XA, RF, SC, US and ES, as zoom, Window & Level variation and presets, measurements and a complete set of diagnostics tools and includes a report tool for the studies.

Lossy compressed mammographic images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA cleared display that meets technical specifications reviewed and accepted by FDA.

VM Clinical Viewer is the web access interface of VM PACS, which allows integration with any Operating System, brings to the institution the capacity to access the images remotely.

VM Clinical Viewer allows the specialist consultation and the communication between radiologists and referring physicians. The system enables authorized external users to have access to specific patient studies, is not intended for diagnostic purposes when used on mobile devices.

VM PACS with VM Medical Workstation is a software composed by a Picture Archiving and Communication System able to receive, transfer and display Dicom Images acquired and send by imaging devices such as CT, MR, CR, DX, MG, US, NM, PET, and other devices, along with a Graphic interface used to configure the PACS and diagnose Medical Images Stored in it.

VM PACS is an affordable and scalable Picture Archiving and Communication System designed to optimize imaging workflow and simplify image management. Use a powerful database engine as client server data. The system architecture scalable, allowing to add storage media according to the volume of studies intended to store and be access online. This gives the possibility to increase gradually the investment in hardware storage as needed. All Image Storage and communications are according to the Dicom Standard. A configuration interface allows the system administrator to set the Application Entity Title (AE-TITLE) and listening port for the Dicom Communications with other devices. To protect the studies PACS has a full-featured fully automatic and programmable backup instance. VM PACS is capable to send query and retrieve studies from other Dicom Applications using the DICOM standard. For Monitoring the Dicom traffic, incorporating a tool which shows all the DICOM transfers in a lapse of time, also VM PACS can be set to auto send studies to other Dicom Application Entities (AEs), depending on the modality, the description, the patient, or any other Dicom Tag selected and also can send all the previous studies from the same patient to a tarqet Dicom AE.

VM Medical Workstation provides all the necessary tools needed for image diagnosis. VM Medical WS allows the user to visualize; report and print images and studies, whit a complete set of tools designed for diagnose medical images. VM Medical Workstation includes a report module to archive the diagnostic results, linked to the informed study. VM Medical WS allows the authorized user to burn studies in a CD/DVD along with the study have an autorun software with diagnostic Dicom tools. If the study has a report, this report is attached to the CD/DVD in a PDF format. The software also include an Advance Printing tool which allows the user to perform a personalized layout, changing window & Level, zoom, and pan to one or all images in the layout.

VM Clinical Viewer is the diagnostic web access interface of VM PACS, which allows integration with any Operating System, and allows the institution to access the images remotely. VM Clinical Viewer improves the specialist consultation and the communication between radiologists and referring physicians from any location in the world. The system enables authorized external users to have access to specific patient studies. VM Clinical Viewer is not intended to be use with diagnostics proposes in mobile devices.

The provided document is a 510(k) premarket notification for a Picture Archiving and Communication System (PACS) with a medical workstation (VM PACS with VM Medical Workstation). This type of device is classified as a Class II medical device (21 CFR 892.2050) and is subject to general controls. The document aims to demonstrate substantial equivalence to a legally marketed predicate device (K123174 CENTRICITY PACS-IW WITH UNIVERSAL VIEWER by GE HEALTHCARE).

Crucially, this document is a regulatory submission for a PACS system, which is a software for storing, displaying, and managing medical images. It is not an AI/ML-driven diagnostic device that would typically have acceptance criteria based on diagnostic performance metrics like sensitivity, specificity, or AUC, nor does it involve a "study that proves the device meets the acceptance criteria" in the sense of a clinical trial demonstrating AI performance.

Instead, the "acceptance criteria" for a PACS system in a 510(k) submission generally revolve around demonstrating compliance with DICOM standards, functional equivalence to a predicate device, and verification of software functionality and safety. The "study" mentioned is not a clinical performance study but rather a demonstration of technical characteristics and functional equivalence.

Therefore, many of the requested points regarding AI/ML performance, sample sizes for test/training sets, ground truth establishment, expert adjudication, and MRMC studies are not applicable to this specific type of device and submission.

Here's how I can address the prompts based on the provided document:

Acceptance Criteria and Device Performance for VM PACS with VM Medical Workstation

Since this is a PACS system and not an AI/ML diagnostic tool, the "acceptance criteria" are derived from demonstrating substantial equivalence to a predicate device and adherence to relevant standards (primarily DICOM). The "reported device performance" refers to the system's demonstrated functionality as described in the submission.

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

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 as it's not a clinical performance study involving a test set of patient data to measure diagnostic accuracy. The "testing" mentioned refers to nonclinical testing of technical characteristics, general function, and application (para 7.3.3).

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 and is not applicable to this type of device submission. There is no "ground truth" establishment in the context of image interpretation performance for a PACS system; its primary function is image management and display.

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

This information is not provided and is not applicable. Since there is no clinical test set for diagnostic performance, there's no adjudication method.

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

There was no MRMC comparative effectiveness study and this is not applicable as the device is a PACS system, not an AI-driven diagnostic aid.

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

This is not applicable. The device is a Picture Archiving and Communication System, which is a foundational system for medical imaging workflow, not a standalone diagnostic algorithm. Human users (physicians, radiologists) interact directly with the software for viewing and interpreting images.

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

This is not applicable. The device's function does not involve making diagnostic claims that would require a "ground truth" for diagnostic performance evaluation.

8. The sample size for the training set

This information is not provided and is not applicable. The device is not an AI/ML model trained on a dataset for diagnostic purposes.

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

This information is not provided and is not applicable.

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BodyViz is a software device that receives digital images and data from various sources (i.e. CT scanner, MRI scanners). Data must be in DICOM format. Images and data are parsed, stored, processed, and displayed within the system as 3D representations. Image tools are available such as translation, rotation, scaling, clipping, and coloring. A data tool is available to window the displayed representation to certain tissue types based on tissue density (e.g., muscle, bone, or skin). Analysis of images and diagnosis is not performed by the software but by physicians or trained professionals.

BodyViz is a software based application for creating 3D models of patient data from 2D scan slices. Users have the ability input, display, color, and manipulate the 2D scan slices via a 3D representation. BodyViz is a visualization environment that allows surgeons to plan various types of surgery on their patient data in 3D. BodyViz works with any, DICOM formatted 2D image scan slices. Data can be accessed from internal and external data storage devices, as well as network and CD/DVD data sources. The software runs on any modern Windows based computer (i.e. laptop or desktop) with a 3D graphics card that meets minimum requirements, eliminating the need for specialized hardware. BodyViz is a medical device image software that is used with computer hardware in a typical hospital or clinic networked computer environment. The device does not contact the patient, nor does it control any life sustaining devices. A physician, providing sufficient opportunity for competent human intervention, interprets images and information being displayed.

The provided text does not contain information about acceptance criteria or a study proving that the device meets such criteria. The document is a 510(k) summary for the BodyViz Picture Archiving Communications System, which is a software device for creating 3D models from 2D scan slices.

The relevant sections discuss:

• Device Description: The software's function to input, display, color, and manipulate 2D scan slices into 3D representations for surgical planning.
• Indications for Use: Emphasizes that "Analysis of images and diagnosis is not performed by the software but by physicians or trained professionals." This indicates the device is a visualization tool, not a diagnostic aid that would require performance metrics for accuracy or sensitivity/specificity.
• Technological Characteristics: States it's medical device image software used with computer hardware in a PACS environment.
• Testing: Only mentions "BodyViz software has been tested to DICOM media storage application profiles." This generally refers to technical standards compliance for image handling, not clinical performance.
• Conclusion: Declares substantial equivalence to predicate devices.

Therefore, I cannot provide the requested table and study details because the provided text does not include:

1. Acceptance Criteria and Reported Device Performance: No specific performance metrics (e.g., accuracy, sensitivity, specificity, processing time) are mentioned, nor are any thresholds for acceptance.
2. Sample size for the test set or data provenance: No clinical study or test set is described beyond DICOM compliance.
3. Number and qualifications of experts for ground truth: Not applicable as no clinical ground truth assessment is mentioned.
4. Adjudication method: Not applicable.
5. MRMC comparative effectiveness study: No such study is mentioned or implied.
6. Standalone performance study: No study evaluating the algorithm's performance on its own is described.
7. Type of ground truth used: Not applicable.
8. Sample size for the training set: Not applicable as no AI/Machine Learning model requiring a training set is explicitly described beyond basic image processing.
9. How ground truth for the training set was established: Not applicable.

The device, as described, is a visualization tool. Its "performance" would likely be assessed through its ability to accurately render 3D models from 2D DICOM data, rather than diagnostic accuracy. The testing mentioned only addresses DICOM compliance, which is a technical interoperability standard, not a clinical performance standard.

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