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The CARESTREAM PACS is an image management system whose intended use is to provide completely scaleable local and wide area PACS solutions for hospital and related institutions/sites, which will archive, distribute, retrieve and display images and data from all hospital modalities and information systems.

The system contains interactive tools in order to ease the process of analyzing and comparing three dimensional (3D) images. It is a single system that integrates the review, dictation and reporting tools that creates a productive work environment for the radiologists and physicians.

The CARESTREAM PACS Lightweight Viewer software program is used for patient management by the referral community in order to access and display patient data, medical reports, and medical images from different modalities including CR, DR, CT, MR, NM and US after the primary reading has been completed on dedicated diagnostic workstations.

The CARESTREAM PACS Lightweight Viewer provides wireless and portable access to medical images for referral purposes. It is not intended to be used as, or to replace, a full diagnostic workstation or system and should be used only when there is no access to a workstation. This device is not to be used for mammography.

CARESTREAM PACS is an image management system whose intended use is to provide completely scalable local and wide area PACS solutions for hospital and related institutions/sites, which will archive, distribute. retrieve and display images and data from all hospital modalities and information systems.

It is a software only solution that contains interactive tools in order to ease the process of analyzing and comparing three dimensional (3D) images. It is a single system that integrates the review, dictation and reporting tools that creates a productive work environment for the radiologists and physicians. It offers an intuitive user interface which includes a tab concept, grouping features, and functions with the same context together such as Image display, Monitor layout, Comparison, Wide area user tab, etc.

The device provides functionality to allow remote site access to image and patient data enabling diagnostic reading through industry standard interfaces. It is designed using an open architecture that allows for various proprietary and off the shelf software components to be integrated with off the shelf hardware components and configured meeting the user's specific needs in a single-site or multi-site environment.

Carestream PACS provides support for 3D registration of studies taken at different times or by different modalities for reading of CT/MRI or PET-CT images. The volumetric data sets are synchronized allowing the user to view reformatted series side by side and superimposed images. In all methods the algorithm is only using a rigid space transformation. Automatic vessel segmentation suggests a segmentation that that can either be accepted, ignored or fine tuned by the user.

The CARESTREAM PACS LightWeight Viewer feature addresses the need for a fast and simple web based tool to access patient records and images. It allows for high speed distribution of image data to users in a wide area setup. The software technology uses HTML5 which allows browser enabled devices to run the application without local software installation. The LightViewer has a simple GUI for viewing including zoom, pan, windowing, basic measurements, cine, etc.

The CARESTREAM PACS Lightweight Viewer provides wireless and portable access to medical images for referral purposes. It provides a diagnostic viewer of medical images substantially equivalent to the CARESTREAM PACS software, with portable device characteristics and functionality. This device is not intended to replace full workstations and should be used only when there is no access to a workstation. The CARESTREAM PACS LightWeight Viewer software is not to be used for mammography in the US.

Here's a summary of the acceptance criteria and the study details for the Carestream PACS, based on the provided document:

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

The document does not explicitly state quantitative acceptance criteria or specific numerical performance results for the device beyond the general statement that "Predefined acceptance criteria was met and demonstrated that the device is as safe, as effective, and performs as well as or better than the predicate device."

However, it does describe the types of tests performed:

• IEC 62563-1
• AA PM TG18 guidelines |
  | Diagnostic Image Viewing Capabilities | Affirmed by multiple studies with qualified radiologists using various modalities (CR, DR, CT, MR, NM, US) under different environmental conditions. |
  | Functionality under Simulated Use Conditions | Testing was conducted under simulated use conditions; predefined acceptance criteria was met. |

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

• Sample Size: Not explicitly stated for the "multiple studies with qualified radiologists."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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)

• Number of Experts: "multiple qualified radiologists" were used. The exact number is not stated.
• Qualifications of Experts: Described as "qualified radiologists." No further details on their experience level (e.g., years of experience, subspecialty) are provided.

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

The document does not describe any specific adjudication method for establishing ground truth or evaluating reader performance.

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

• MRMC Study: The document does not describe a multi-reader, multi-case (MRMC) comparative effectiveness study. The studies mentioned focus on affirming the diagnostic image viewing capabilities of the LightWeight Viewer itself, rather than comparing human reader performance with and without AI assistance.
• Effect Size: Not applicable as no such study is described. The device is a PACS viewer, not an AI-assisted diagnostic tool in the sense of providing automated interpretations.

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

The device is a PACS viewer with interactive tools, not a standalone algorithm providing diagnostic outputs. Therefore, a standalone algorithm-only performance study as typically understood for AI diagnostic tools would not be relevant or described here. Its functionality aims to ease the process of human analysis.

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

The document implies that the "diagnostic image viewing capabilities" were affirmed by "qualified radiologists," suggesting an expert-based evaluation. However, the specific method for establishing a definitive "ground truth" (e.g., consensus, comparison to gold standard, pathology) for the images used in these studies is not detailed.

8. The sample size for the training set

The document does not mention a training set, as the device primarily describes software modifications to an existing PACS system and the development of a viewer. It is not an AI algorithm that would typically require a training set for model development.

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

Not applicable, as no training set is mentioned in the context of this device.

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The CARESTREAM PACS is an image management system whose intended use is to provide completely scaleable local and wide area PACS solutions for hospital and related institutions/sites, which will archive, distribute, retrieve and display images and data from all hospital modalities and information systems.

The system contains interactive tools in order to ease the process of analyzing and comparing three dimensional (3D) images. It is a single system that integrates the review, dictation and reporting tools that creates a productive work environment for the radiologists and physicians.

Carestream PACS, a multi-modality radiology reading and reporting station, provides support for 3D registration of studies taken at different times or by different modalities (CT and MRI), and for reading PET-CT images. The volumetric data sets are synchronized allowing the user to view reformatted series side by side and superimposed images. In all methods the algorithm is only using a rigid space transformation.

The registration in the "Volume Matching" application can be created by these methods:

. Register Studies

Full Automatic registration: The user is able to register two datasets by simply clicking on the windows that present these datascts, with no other additional input. The algorithm will not take into account the exact clicking points.

• Manually Register Studies .
  The user is able to "manually" register two datasets by providing additional inputs to the registration algorithm. The matching of the two volumes is done based on the planes of the two studies and on the position of the two clicking points. This input should be enough to match the two dataset. The user should try to click on the same anatomical location in both studies. while both planes are already swiveled to represent the same anatomical plane.

• . Refine Registration
  The user is able to adjust the registration. The refine algorithm is actually a local registration algorithm that tries to match two volumes based on local, rather than global, similarity. Practically, it can serve the user when he/she wants to focus on a certain region and to compare it in the two datasets. In such cases, local registration is preferable, even if it means that the global registration will be of a poorer quality.

• Treat Studies as Registered .
  The application supports the "treat studies as registered" feature - the ability for the user to select datasets / all datasets for which the system should treat as if they are fully registered to one another. This is needed to support places where although the data doesn't have the same frame of reference, they were registered in a "black-box" and only then copied to the PACS. The algorithm is ignoring the exact clicking point.

Reading PET-CT images require both specific image-manipulation capabilities and standardized uptake value presentation. PET scanning utilizes a radioactive molecule that is similar to glucose, called fluorodeoxyglucose (FDG). FDG accumulates within malignant cells because of their high rate of glucose metabolism, and is currently the radiotraccr most commonly used for PET' imaging.

The standardized uptake value (SUV) is a scmi-quantitative value that allows expression of FDG untake in a lesion relative to the injected dose. The standardized uptake value (SUV) is defined as the ratio of activity in tissue per millimeter to the activity in the injected dose per patient body weight.

Here's an analysis of the provided text regarding the Carestream PACS, focusing on acceptance criteria and study details.

The provided document K083673 for Carestream PACS is a 510(k) summary, which for this particular device, focuses on substantial equivalence to a predicate device rather than a detailed clinical effectiveness study with specific performance metrics and acceptance criteria for a novel AI algorithm. The device, described as a multi-modality radiology reading and reporting station supporting 3D registration and PET-CT image reading, is a software modification to an existing PACS system.

Therefore, many of the requested points regarding acceptance criteria, AI performance, ground truth, and extensive study details typically associated with AI-driven diagnostic devices are not explicitly present in this type of submission. The focus is on verifying that the modified software performs as safely and effectively as the predicate device (Mirada Solutions Ltd, Fusion 7D).

Here's an attempt to extract the information based on the provided text, noting where information is absent:

Acceptance Criteria and Study Overview for Carestream PACS (K083673)

Summary:
The Carestream PACS device (K083673) is a modified PACS software, not a novel AI diagnostic algorithm. The 510(k) submission focuses on demonstrating substantial equivalence to a predicate device (Mirada Solutions Ltd, Fusion 7D) through performance testing under simulated use conditions. Explicit acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy) are not defined for the software's functionalities as a primary endpoint in this document. Instead, the acceptance criterion revolved around meeting design input requirements and conforming to user needs, demonstrating comparable safety and effectiveness to the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified in the provided text. The document refers to "nonclinical testing was conducted under simulated use conditions," but provides no details on the number of cases or studies used for testing.
• Data Provenance: Not specified. It's likely simulated or internal test data given the "simulated use conditions" and non-clinical nature of the testing mentioned.

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

• This information is not provided in the summary. For this type of 510(k) for a PACS workstation modification, it's less likely that a formal expert-adjudicated ground truth dataset was created, as the focus is on software functionality and comparability rather than diagnostic accuracy of a new clinical claim. Technical experts and software testers would have been involved in verifying functionality.

4. Adjudication Method for the Test Set

• Not applicable / Not specified. Given the nature of the device and testing, a formal adjudication method (like 2+1 or 3+1) for clinical performance metrics is not mentioned or implied. Testing likely involved verifying the correct output of the registration algorithms and display of PET-CT images against expected results, rather than interpreting a "positive" or "negative" clinical finding.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No. An MRMC study comparing human readers with AI assistance versus without AI assistance was not done and is not mentioned. This submission pertains to a PACS software update, not an AI-driven interpretive algorithm designed to directly improve human reader performance or make clinical decisions.

6. If a Standalone (Algorithm Only) Performance Study was done

• Partially applicable, but for functionality rather than diagnostic accuracy. The "Discussion of Testing" mentions "Performance testing was conducted to verify the design output met the design input requirements." This implies standalone testing of the software functionalities (3D registration, PET-CT display and SUV calculation) to ensure they worked as intended. However, this is distinct from a standalone diagnostic performance study (e.g., measuring sensitivity/specificity of an AI model). The summary points to the algorithm's ability to perform rigid space transformations, automatically register datasets, allow manual registration and refinement, and calculate SUV values, which were verified functionalities.

7. The Type of Ground Truth Used

• Internal specifications and engineering verification. For the functionalities described (3D registration, PET-CT display, SUV calculation), the "ground truth" would likely be defined by the technical specifications for these features. For example, for 3D registration, "ground truth" would involve known transformations or successful alignment verified by technical means. For SUV calculation, it would be the correct application of the defined formula and display. It is not expert consensus, pathology, or outcomes data related to disease diagnosis.

8. The Sample Size for the Training Set

• Not applicable / Not specified. This device is a PACS system with image manipulation tools, not a machine learning model that requires a "training set" in the conventional sense of AI development for diagnostic tasks. The algorithms (e.g., for rigid registration) are likely rule-based or optimized through engineering, not trained on large clinical datasets in a machine learning paradigm.

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

• Not applicable. As a training set for an AI model is not indicated, the method for establishing ground truth for such a set is irrelevant in this context.

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The KODAK CARESTREAM PACS is an image management system whose intended use is to provide completely scaleable local and wide area PACS solutions for hospital and related institutions/sites, which will archive/distribute/retrieve and display images and data from all hospital modalities and information systems.

Not Found

The provided document is a 510(k) clearance letter for the "Kodak Carestream PACS" device. This letter indicates that the device has been found substantially equivalent to a legally marketed predicate device.

However, this document does not contain information about acceptance criteria, device performance studies, sample sizes, expert qualifications, or ground truth establishment.

The 510(k) clearance process primarily focuses on demonstrating substantial equivalence to a predicate device, which often relies on comparison of technical characteristics and intended use, rather than extensive new clinical performance studies. While some performance testing is implied for regulatory submission, the details of such testing are not included in this particular FDA letter.

Therefore, I cannot provide the requested information from the given text. A complete description of acceptance criteria, study details, and performance metrics would typically be found in the manufacturer's 510(k) submission itself, not in the FDA's clearance letter.

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