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Prima is a software application that is intended for use in receiving, processing, manipulating, displaying, printing, and archiving mammography images as well as other medical images and data (e.g. US and MR). Images and data can be stored, communicated, and displayed within the system or across computer systems. Prima provides various image processing and measurement tools to facilitate the interpretation of mammography x-ray, breast tomosynthesis, and other multimodality DICOM medical images and enable diagnosis. Lossy compressed mammographic images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA cleared monitor that meets technical specifications reviewed and accepted by the FDA.

Prima is typically used by trained professionals, including radiologists, oncologists, surgeons, technologists and clinicians and may provide information to be used for screening and diagnostic procedures.

Prima is a software application that is intended for use in receiving, processing, manipulating, displaying, printing, and archiving mammography images as well as other medical images and data (e.g. US and MR). Images and data can be stored, communicated, and displayed within the system or across computer systems. Prima provides various image processing and measurement tools to facilitate the interpretation of mammography x-ray, breast tomosynthesis, and other multimodality DICOM medical images and enable diagnosis. Lossy compressed mammographic images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA cleared monitor that meets technical specifications reviewed and accepted by the FDA.

Prima is typically used by trained professionals, including radiologists, oncologists, surgeons, technologists and clinicians and may provide information to be used for screening and diagnostic procedures.

The provided text is a 510(k) summary for a Picture Archiving and Communication System (PACS) software called "Prima." It declares substantial equivalence to predicate devices but does not contain information about specific acceptance criteria, performance studies, or clinical trial data as requested. The summary mentions that the performance of the software was tested in accordance with Hologic's design control procedures to demonstrate intended performance and that testing was successfully conducted, and demonstrates that Prima meets all of its functional requirements and specifications. However, it does not provide details about these tests.

Therefore, I cannot fulfill your request for the specific acceptance criteria and study details. The document focuses on regulatory equivalence rather than detailed performance metrics.

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CURVE IMAGE is an internet-based, image management software (PACS) that enables dental offices to keep records of hard and soft tissue charts in a form of digital images. The system uses a Web-based interface and includes acquisition, editing and storage of digital images. Images and data can be stored, communicated, processed and displayed within the system or across computer networks at distributed locations. Images can be acquired from standard dental imaging devices, or can be uploaded directly from the user's computer. Images can be edited (e.g., zoomed, contrast, rotated, etc.), as well as exported. The system is designed to provide images for diagnostic use.

Curve Image is a web-based dental practice image management system that provides Internet access to an image library for each patient in a practice. It also provides tools for dental practices to manipulate patient images and both acquire and upload new images in industry standard formats. Images can be annotated and tagged, and are available for both diagnostic and non-diagnostic use. All of these actions are performed from a secure website, after entering user credentials, similar to other commonly used online applications. The system can be accessed by the user via an internet connection and does not require any software installation on the user's computer.

Curve Image may be sold as a stand-alone product, or may be bundled with a suite of web applications to form a comprehensive dental practice management application called HERO.

The provided text describes a 510(k) submission for the "Curve Image" dental PACS system. However, the document does not contain specific acceptance criteria, performance metrics (like sensitivity, specificity, accuracy), sample sizes for test or training sets, details on expert ground truth establishment, or clinical study results typical for AI/ML device evaluations.

The document focuses on demonstrating substantial equivalence to predicate devices by comparing intended use, environment of use, performance characteristics, and technological characteristics. This type of submission relies on the similar nature of the device to already-cleared devices, rather than a clinical trial demonstrating new performance.

Therefore, many of the requested sections regarding acceptance criteria and study details cannot be filled from the provided text.

Here's an analysis of what information is available:

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

• Acceptance Criteria: Not explicitly stated in terms of performance metrics (e.g., sensitivity, specificity, F1 score, AUC). The "acceptance criteria" for this 510(k) is implicitly demonstrating substantial equivalence to the predicate devices through a comparison of features and capabilities, and successful non-clinical testing.
• Reported Device Performance:
  • Non-Clinical Testing Summary: "Curve Dental, Inc. has conducted extensive non-clinical and validation testing of the Curve Image system, as a PACS system that is capable of providing reliable post-processing and display of images for dental applications. All of the different components of the Curve Image software have been stress tested to ensure that the system as a whole provides all the capabilities necessary to operate in a manner substantially equivalent to the predicate devices."
  • Tests performed included:
    • Product Risk Assessment
    • Software modules verification tests
    • Software validation test

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

• Not provided. This document does not detail specific image datasets used for a "test set" in the context of clinical performance evaluation. The non-clinical testing is described generally as "software modules verification tests" and "software validation test" without specifying sample sizes for images or data.
• The device is a PACS system, so it manages images from "standard dental imaging devices" or uploads from the user's computer, implying a wide range of potential data, but not a specific test set.

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)

• Not applicable. This information is not relevant to a 510(k) submission for a PACS system that acts as an image management tool, not an AI diagnostic aid requiring expert-established ground truth for its own diagnostic performance. The device is for "diagnostic use" by dental professionals, but it doesn't perform automated diagnosis itself.

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

• Not applicable. See point 3.

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. This type of study is not mentioned or undertaken for this device. The Curve Image system is a PACS, not an AI diagnostic tool designed to assist human readers directly in interpretation (beyond displaying images clearly and providing basic manipulation tools).

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

• Not applicable. The device is a Picture Archiving and Communications System (PACS); it is not an AI algorithm performing diagnostic tasks. Its performance is related to its functionalities as an image management system.

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

• Not applicable. For a PACS system, the "ground truth" would relate to its ability to accurately store, retrieve, display, and manipulate images, which is assessed through software validation and verification, not against clinical ground truth like pathology for specific diagnoses.

8. The sample size for the training set

• Not provided. The device is not an AI/ML system that undergoes "training" in the traditional sense on a dataset. It's a software application.

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

• Not applicable. See point 8.

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Zenis is intended for viewing of images acquired from Fluoroscopic X-ray system when installed on suitable commercial-standard PC hardware. Zenis is intended for use as a primary diagnostic and analysis workstation in Radiology or other departments. It is also intended for use as a clinical review workstation throughout the healthcare facility and may be part of a larger PACS configuration.

Zenis is a PC-based DICOM workstation platform which provides scalable image and data management solutions for medical imaging. This software-based product provides capabilities for the acceptance, transmission, printing, display, storage, editing and digital processing of medical images and associated data. Zenis may be combined with a PACS network or connected directly to a modality through the use of DICOM networking.

The provided text is a 510(k) summary for the Zenis Picture Archiving and Communications System (PACS). This document primarily focuses on establishing substantial equivalence to a predicate device (GE Medical Systems Information Technologies RA600) rather than providing detailed acceptance criteria and a study demonstrating the device meets those criteria in a typical AI/ML medical device context.

Based on the provided text, the following points can be extracted or reasonably inferred, but many of the requested details are not available as this document is not a performance study report for a novel AI algorithm.

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

The document does not explicitly state quantitative acceptance criteria for device performance as would be found in a study for a new diagnostic algorithm. Instead, it relies on the concept of substantial equivalence to an existing predicate device.

2. Sample size used for the test set and the data provenance

The document mentions "performance test and clinical evaluation" but does not provide any details on the sample size used, the type of data (e.g., specific image modalities or pathology types), or its provenance (country of origin, retrospective/prospective). This is typical for a PACS system which is a general image management and viewing device, not a diagnostic algorithm analyzed for specific performance metrics on a dataset of cases.

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

This information is not provided. As the device is a PACS system for image viewing and management, the concept of "ground truth" as it applies to diagnostic accuracy for an AI algorithm is not directly relevant here. The evaluation would likely focus on system functionality, image quality for viewing purposes, and adherence to DICOM standards, not diagnostic accuracy against a ground truth.

4. Adjudication method for the test set

This information is not provided. See the explanation for point 3.

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 was done or reported. The Zenis device is a PACS system, not an AI-assisted diagnostic tool. Therefore, the concept of improving human readers with AI assistance does not apply in this context.

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

Not applicable. The Zenis device is a PACS system, not a standalone diagnostic algorithm.

7. The type of ground truth used

Not applicable in the context of diagnostic accuracy. For a PACS system, "ground truth" would relate to the accurate display, storage, and transmission of medical images as per DICOM standards and clinical requirements. The document implies that the "performance test and clinical evaluation" would have verified these aspects.

8. The sample size for the training set

Not applicable. As a PACS system, this device is not an AI/ML algorithm that undergoes a "training" phase with a specific dataset.

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

Not applicable. See explanation for point 8.

In summary:

The provided 510(k) summary for the Zenis PACS focuses on establishing substantial equivalence based on the device's functions and characteristics being "almost same" as the predicate device (RA600). It makes a general claim that "performance test and clinical evaluation indicates that the new device is as safe and effective as the predicate devices," but it does not provide any specific details about the methodology, data sets, expert involvement, or quantitative results that would typify a study validating a diagnostic AI device against explicit acceptance criteria and ground truth.

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Triana is intended for use as a software package which obtains medical images from CT, Cephalometric / Panoramic X-ray system & etc., stores those and provides 3D visualization, 2D analysis, various MPR(Multi-Planar Reconstruction) functions for further rapid and precise diagnosis.

Triana is a computer based dental imaging software which obtains medical images taken from CT, Cephalometric / Panoramic X-ray system & etc. and provides 3D visualization and 2D analysis, various MPR (Multi-Planar Reconstruction) functions for further rapid and precise diagnosis.

Triana is designed to provide users easy and familiar user-interface. Also Triana makes it possible to manage medical images more easily and provides advanced tools for 2D and 3D analysis with rendering functions.

The provided text describes a 510(k) submission for a Picture Archiving and Communications System (PACS) called Triana. However, it does not contain any information regarding specific acceptance criteria, a study proving device performance against those criteria, or details about sample sizes, expert qualifications, or ground truth establishment.

The submission focuses on establishing substantial equivalence to a predicate device (CyberMed, Inc.'s OnDemand3D K070464). The text explicitly states:

"The result of bench and clinical evaluation indicates that the new device is as safe and effective as the predicate devices."

This statement implies that some form of evaluation was performed, but no details of such evaluation (e.g., acceptance criteria, detailed study results, methodologies) are provided in the given document excerpts.

Therefore, I cannot populate the requested table and answer the questions based on the input.

Key takeaway: The provided document is a 510(k) submission primarily focused on demonstrating substantial equivalence to a predicate device based on similar functions and characteristics, rather than a detailed report of a study with specific acceptance criteria and performance metrics.

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The vRad™ PACS software is used with general purpose computing hardware, which meets or exceeds minimum specifications. vRad™ PACS software is intended to receive, transmit, store and display images for clinical purposes. The vRad™ PACS Viewer component is intended for installation on an off-the-shelf PC, meeting or exceeding minimum specifications and networked with vRad™ PACS Storage component. The vRad™ PACS Viewer is intended to serve as the primary user interface for the processing of medical images for presentation on displays appropriate to the medical task being performed. The vRad™ PACS Viewer can process medical images from DICOM modalities such as X-ray radiography, X-ray computed tomography, magnetic resonance imaging, ultrasound, nuclear medicine, and images from other DICOM compliant modalities.

The vRad™ PACS system should not be used for Mammography primary image diagnosis.

vRad™ PACS is a device which consists solely of software and allows electronic transmission of radiological patient images from one location to another. The device has the capability to accept, transfer, display, store, and digitally process medical images to trained and qualified radiologists for the purposes of providing digital diaging The software provides functions for performing operations interpretation services. related to image manipulation, enhancement, compression, and quantification of medical images (except mammography images). The software enables the user to display 3D maximum intensity projection (MIP) and Multi-Planar Reformatting (MPR) visualization of study images.

The provided text describes a 510(k) summary for the vRad™ Picture Archiving and Communications System (PACS) Software. However, it does not include information about specific acceptance criteria or a study that proves the device meets such criteria.

The document is a regulatory submission for a device, not a report on its performance against specific acceptance criteria. It focuses on demonstrating substantial equivalence to predicate devices based on design, technical requirements, and intended use, which is the core of a 510(k) submission.

Therefore, I cannot extract the requested information as it is not present in the provided text. The document does not describe performance metrics, sample sizes for test or training sets, expert qualifications, adjudication methods, or results of comparative effectiveness studies or standalone performance studies.

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The intended use of the KinetDx, is for the acceptance, transfer, display, storage, and digital processing of diagnostic ultrasound, CT, MRI, and X-ray angiography images, including manipulation and quantification of the images.

KinetDx is a picture archival and communications system (PACS) that includes a dedicated DICOM server used for image and data storage, retrieval, and archiving. The server accepts images and data from DICOM acquisition devices attached to it by way of a network. The system also includes one or more workstations networked with the server, which are used for clinical review of images and data. Workstations also allow editing of patient demographic and clinical data, image manipulation, and preparation of clinician's reports.

The provided text is a 510(k) summary for the KinetDx Medical Image Management Device, a PACS system. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, this document does not contain information about acceptance criteria or a study demonstrating the device meets those criteria, as typically found in clinical validation studies.

PACS systems function as image management devices rather than diagnostic algorithms that produce specific findings requiring clinical performance metrics. Their "performance" is generally related to their ability to correctly store, retrieve, display, and process images, and their interoperability with other medical devices.

Therefore, most of the requested information regarding acceptance criteria, study design, expert involvement, and ground truth cannot be extracted from this particular 510(k) summary.

Here's an assessment based on the available information:

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

   • Not provided. This type of 510(k) summary for a PACS device typically focuses on substantial equivalence to predicate devices, and the functional characteristics rather than performance metrics against specific clinical acceptance criteria.

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

   • Not provided. No specific test set for evaluating diagnostic performance is mentioned. The device's "performance" would likely be assessed through engineering verification and validation (e.g., image integrity, speed of retrieval, display accuracy), which are not detailed here.

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):

   • Not applicable. As no clinical performance study involving diagnostic accuracy is described, there's no mention of experts establishing ground truth for such a study.

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

   • Not applicable. No clinical performance study is described.

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:

   • Not applicable. This device (KinetDx) is a PACS system for image management, not an AI-powered diagnostic algorithm. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device's function.

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

   • Not applicable. KinetDx is an image management system, not a standalone diagnostic algorithm.

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

   • Not applicable. No clinical performance study requiring ground truth is described.

8. The sample size for the training set:

   • Not applicable. KinetDx is a PACS system; it does not explicitly use a "training set" in the machine learning sense to develop its core functionality. Its software is developed and tested through traditional software engineering processes.

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

   • Not applicable.

Summary based on available information:

The KinetDx is a Picture Archival and Communications System (PACS). Its 510(k) submission focuses on demonstrating substantial equivalence to predicate PACS devices (EchoLink, ALI UltraPacs, ALI DataComPacs Module) based on technological characteristics (standard computer hardware, proprietary software, Windows 2000 OS, optional DAP card for faster decompression/display) and intended use (acceptance, transfer, display, storage, digital processing, manipulation, and quantification of diagnostic ultrasound, CT, MRI, and X-ray angiography images).

The document does not contain details of specific performance criteria or a clinical study to prove diagnostic accuracy, as this is not the primary function of a PACS system. Its "performance" would be related to its engineering validation and verification, ensuring it reliably manages and displays images as intended and substantially equivalent to predicate devices.

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