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InstaRad™ is a software device (DICOM Gateway Application, InstaRad Enterprise Server, and Workstation) used for viewing and manipulating medical images. Digital images (including mammography and data from various sources (including CT, MR, US, RF units, computed and direct radiographic devices, secondary capture devices, scanners, imaging gateways, or imaging sources) can be displayed, processed, stored and communicated across computer networks using this software. InstaRad™ can be integrated with an institution's existing Hospital Information System (HIS) or Radiology Information System (RIS) based on the study of the System, providing seamless access to reports for fully-integrated electronic patient records.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretation. Mammographic images may only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

InstaRad™ is an application used for viewing and manipulating medical images. Digital images and data from various sources (including CT, MR, US, RF units, computed and direct radiographic devices, secondary capture devices, scanners, imaging gateways, or imaging sources) can be displayed, processed, stored and communicated across computer networks using this software. When viewing images, users can perform adjustments of window width and level, annotation, and various image manipulations. In addition, InstaRad™ can be integrated with an institution's existing Hospital Information System or Radiology Information System (based on the study of the System), providing seamless access to reports for fully-integrated electronic patient records.

InstaRad™ allows multiple centers or hospitals to send their images to a central server where Radiologists can view images over Web. It consists of following components:

• DICOM Gateway Application is deployed at all remote centre or . hospitals. It receives images from modalities over LAN and uploads them to central server.
• Central Server runs the Web Server and Image Server and provide . study and image data to doctors.
• . WorkStation - Doctor at client side access the study list in the browser. They can select the patient and download the images.

The provided text describes InstaRad™, a Picture Archiving Communications System (PACS), and its 510(k) summary for regulatory approval. However, the document does not contain any information about acceptance criteria, device performance studies, sample sizes, expert involvement, or ground truth establishment related to the device itself as a medical image processing system.

The text focuses on establishing substantial equivalence to a predicate device (VISAGE PACS/CS, RELEASE VERSION 4.1, K062490) based on its technological characteristics, intended use, and general safety and effectiveness.

Therefore, I cannot provide a table of acceptance criteria and reported device performance from the given information because that data is absent from the document. The subsequent points regarding studies are also not addressable for the same reason.

Here's a breakdown of what the document does provide and why the requested information is missing:

• Device Description: InstaRad™ is an application for viewing, manipulating, processing, storing, and communicating digital medical images from various sources (CT, MR, US, RF, etc.) across computer networks. It can be integrated with HIS/RIS.
• Intended Use: Viewing and manipulating medical images, including mammography (with specific caveats regarding lossy compression and monitor requirements).
• Technological Characteristics: It's a software product handling digital medical images, not contacting the patient, and not controlling life-sustaining devices. Human intervention is expected for interpretation.
• Predicate Device: VISAGE PACS/CS, RELEASE VERSION 4.1 (K062490).
• Conclusion: The submission contains adequate information to determine substantial equivalence to the predicate device.

Missing Information:

1. Acceptance criteria and reported device performance: Not specified. This type of detail is often found in performance testing reports, which are not included in this 510(k) summary.
2. Sample sizes used for the test set and data provenance: No performance test data is mentioned.
3. Number of experts used to establish ground truth for the test set and their qualifications: Not mentioned.
4. Adjudication method: Not mentioned.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: Not mentioned.
6. Standalone (algorithm-only) performance: Not mentioned, as this device functions as a PACS, not an AI algorithm performing a diagnostic task.
7. Type of ground truth used: Not mentioned.
8. Sample size for the training set: Not applicable for this type of PACS device, as it doesn't appear to use machine learning for diagnostic tasks that would require a "training set."
9. How the ground truth for the training set was established: Not applicable.

The 510(k) process for a PACS system like InstaRad™ typically focuses on functional equivalence, compliance with DICOM standards, security, data integrity, and display capabilities compared to an existing predicate. It's not usually about diagnostic accuracy studies with ground truth in the same way an AI diagnostic algorithm would be evaluated.

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Visage PACS is a system for distributing, viewing, and processing medical images and reports within and outside health care environments. It is to be used only by trained and instructed health care professionals. Visage PACS consists of the following components:

Visage PACS Storage: Visage PACS Storage offers an archiving option for long-term storage of image data.

Visage PACS Web: Data that are stored on the Visage PACS Storage server can be accessed simultaneously by multiple web-based viewing stations within or outside a healthcare enterprise through web clients.

Visage CS: Visage CS is a client server system that uses thin client technology for distribution of 3D image data generated from image data of state-of-the-art scanning modalities.

Integration with other hospital information systems (HIS, RIS, CIS) is provided via special interfaces.

Only DICOM for presentation images can be used on an FDA approved monitor for mammography for primary image diagnosis. Only uncompressed or non-lossy compressed images must be used for primary image diagnosis in mammography.

Visage PACS is a system to distribute, view, and process medical images and reports within and outside of health care environments. It consists of the following components:

• Visage PACS Storage 1
• -Visage PACS Web
• Visage CS -

Visage PACS Storage: A server receives image data in DICOM format via the hospital network. This provides universal connections to archives, modalities and workstations. The modalities that are supported by Visage PACS Storage are listed in the DICOM Conformance Statement. Visage PACS Storage offers an archiving option for long-term storage of image data.

Visage PACS Web: Data that are stored on the Visage PACS Storage server can be accessed simultaneously by multiple web-based viewing stations within a healthcare enterprise or from elsewhere outside through web clients. The image data transfer is done in DICOM format via the Intranet or the Internet. Images can be viewed directly within a web browser (Internet Explorer). The system offers simple functions for image manipulation and measurements. Reports can be viewed together with the images on one page.

Visage CS: Visage CS is a client server system that uses thin client technology for distribution of 3D image data generated from image data of state-of-the-art scanning modalities. The thin client viewer allows to view and process 3D image data. No DICOM data is transferred to the client. Instead of image data, a stream of compressed screen content information is transmitted during interaction.

Modification: Implementation of the cardiac analysis option. With the new version of the software the user can import cardiac CT time series in order to utilize them for the LV analysis (analysis of the left ventricle). The LV analysis tool card helps with the analysis of the left ventricle in cardiac CT time series. This toolcard quides the user through an LV analysis step by step. The LV tools support the physician in finding the left ventricle in the image data. The LV tool card supports the user to find and display the long and short axes of the left ventricle and to segment the left ventricle. It supports the user to calculate the total volume of the left ventricle over one heart cycle, the stroke volume and the ejection fraction of the various LV regions and the accumulated wall motion.

The provided 510(k) summary for K072205 (VISAGE PACS/CS 5.0) focuses on demonstrating substantial equivalence to a predicate device (AquariusNet Server/Thin client K012086) for its new cardiac analysis and other imaging features. It does not contain a detailed study with acceptance criteria and reported performance metrics in the format typically used for AI/ML device evaluations.

Instead, the submission emphasizes validation and effectiveness through a risk analysis, risk management plan, and software development process, stating that "hazard analysis, verification and validation tests (according to our software development process) and evaluations by hospitals" have been performed. However, specific quantitative acceptance criteria or detailed study results with sample sizes, expert qualifications, or ground truth methodologies are not included in the provided text.

The closest information provided related to "acceptance criteria" and "reported performance" is in the Substantial Equivalence Comparison Chart, which indicates the presence or absence of certain features in the new device and the predicate device. This is a functional comparison, not a performance study in the sense of accuracy, sensitivity, or specificity.

Given the information provided, here's what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

No specific quantitative acceptance criteria or reported performance metrics (e.g., sensitivity, specificity, accuracy) are provided in the document. The comparison chart below shows feature equivalence, not performance against specific clinical thresholds.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The provided document does not specify the sample size used for any test set or the data provenance for validation. It mentions "evaluations by hospitals" in a general sense but no details are given.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The provided document does not specify the number or qualifications of experts used to establish ground truth for any test set.

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

The provided document does not describe any adjudication method for a test set.

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

No MRMC comparative effectiveness study is mentioned or detailed in the provided 510(k) summary. This submission predates the common requirement for such studies for many AI/ML devices.

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

The device, VISAGE PACS/CS, is a Picture Archiving and Communications System (PACS) with advanced visualization and analysis tools. Its description states: "A physician, providing ample opportunity for competent human interprets images and information delivered by VISAGE PACS/CS." This indicates that the device is intended for human-in-the-loop use. A standalone algorithm-only performance study is not described or implied. The focus is on providing tools to assist a human interpreter.

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

The provided document does not specify the type of ground truth used for any evaluations.

8. The sample size for the training set

The provided document does not specify details about a training set or its sample size. This is typical for submissions preceding the specific requirements for AI/ML device training data.

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

The provided document does not specify how ground truth for a training set was established, as it does not explicitly mention a training set.

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