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Change Healthcare Stratus Imaging PACS is a software-based medical image management and processing system intended to process and display medical images for the purposes of diagnostic viewing and interpretation by qualified and trained healthcare professionals, including, but not restricted to, radiologists and non-radiology physicians.

The Diagnostic Viewer of Change Healthcare Stratus Imaging PACS provides medical image postprocessing functions such as image manipulation and enhancement that are intended for use in the clinical image review and analysis of medical images acquired through DICOM-compliant imaging devices or IT interfaces.

Change Healthcare Stratus Imaging PACS can be used as a full-featured medical image management and processing system or as an independent viewer in clinical settings.

Only uncompressed or non-lossy compressed DICOM images can be used for primary image diagnosis and interpretation for mammography using monitors intended for mammography display and cleared by the requlatory authority in your region or jurisdiction.

Change Healthcare Stratus Imaging PACS is not intended for diagnostic use on mobile devices.

Change Healthcare Stratus Imaging PACS is a software-based medical image management and processing system designed to perform the necessary functions required for diagnosis and interpretation of medical information. It is intended to be used by qualified and trained radiology physicians for the purpose of assisting in diagnosis and interpretation of medical images.

Change Healthcare Stratus Imaging PACS provides users with a zero-footprint, browser-based diagnostic viewer capable of directly displaying diagnostic quality DICOM standard images, reports and discrete data information acquired by various data sources. The diagnostic viewer provides users with tools and features, including measurement, annotations, comparison, and digital processing of medical images, such as image manipulation, enhancement, and 3D/4D visualization.

Change Healthcare Stratus Imaging PACS functionality, such as storage, import, sharing, retrieval, and display, that supports the day-to-day operations of qualified and trained healthcare professionals, such as technologists and PACS administrators.

Change Healthcare Stratus Imaging PACS uses a cloud-based architecture offering zero-footprint deployment in hospitals, clinics imaging centers and other healthes. Authorized users from both clinical facilities and remote locations can directly access Change Healthcare Stratus Imaging PACS.

Change Healthcare Stratus Imaging PACS operates on approved web browsers running on commercially available hardware that meets approved specifications.

Change Healthcare Stratus Imaging PACS is designed to integrate with third party, off-the-shelf software through Application Programming Interfaces (APIs) and supported standards (for example, HL7, DICOM) to allow connectivity with systems used in the clinical environment such as reporting tools, EMRs, and advanced visualization tools.

This document (K241981) describes a 510(k) submission for the Change Healthcare Stratus Imaging PACS device, which is a software-based medical image management and processing system. However, it explicitly states on page 6 that "Non-clinical tests, such as mechanical engineering performance testing, are not applicable to the subject device, as Change Healthcare Stratus Imaging PACS is software-only device (SaMD)."

Furthermore, the document states: "Software verification and validation were completed to ensure that the features conformed to all new and previously defined specifications and any risks were properly mitigated, and that the modifications did not degrade the existing functionality. All the software specifications have met the acceptance criteria and the software ad all product release criteria. The testing results did not raise new or different questions of safety and effectiveness other than those already associated with the predicate device."

This indicates that software verification and validation were performed against internal specifications and product release criteria. The document does NOT describe a study that uses a test set with ground truth established by experts to prove the device meets specific acceptance criteria in terms of clinical performance (e.g., diagnostic accuracy, sensitivity, specificity). It focuses on substantial equivalence to a predicate device (K203249) and validation of software functionality rather than a clinical performance study.

Therefore, I cannot provide the requested information from the provided text, as the document does not contain details of such a study.

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Change Healthcare Anatomical AI is intended to analyze pixel data from CT or MR images to create comprehensive anatomic descriptors for export to integrated healthcare systems.

This supplements traditional methods used for the selection, presentation or analysis of image based medical data. The application is intended to enable physicians, or other healthcare providers as well as integrated healthcare systems to rapidly identify images, series, and/ or studies of interest.

Change Healthcare Anatomical AI is not indicated for patients under the age of 18 years old.

Change Healthcare Anatomical AI is a standalone image processing software application that analyzes CT and MR DICOM images to associate anatomic regions with images and exports the derived information for use in integrated healthcare systems. These anatomic descriptors can be applied by integrated applications to categorize anatomy from a patient's CT or MR image, series, or study.

The device communicates via Application Programmable Interfaces (APIs) which allow for receiving DICOM images and returning inference results. The algorithm produces a JSON file which contains results of the analysis for each image and study with the corresponding identified body regions.

Change Healthcare Anatomical AI works in parallel to and in conjunction with the standard of care workflow. The device does not alter the original medical image in any way. The anatomic descriptors are used as supplemental metadata for a patient's imaging study.

Change Healthcare Anatomical AI contains the following core components:

API endpoints
The device uses API endpoints which allow for receiving DICOM images and returning results.

Following receipt of an image, the device performs data validation to ensure appropriateness and compatibility for the algorithm. If the validation fails and the image cannot be processed, an error is returned with the corresponding code and description.

AI algorithm
After validation, the algorithm analyzes the CT or MR image pixel data and generates the anatomic descriptors.

Study results aggregator
The results of the analysis for each image in a study are aggregated and returned to the integrated system.

Data store
The results of the inference for each analyzed image are maintained in a persistent data store. The results are stored by the algorithm inference model and retrieved by the study results aggregation component.

This document describes the regulatory submission for Change Healthcare Anatomical AI (K210719), an image processing software that analyzes CT and MR images to identify anatomical regions.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly state numerical acceptance criteria for a clinical study. However, it mentions that "Software verification testing assessed the performance of the software's anatomical structure detection function, performance characteristics of the algorithm including image-level accuracy..." and that "Test Summary Reports have been created to evaluate the acceptability of test results and all applicable verification and validation activities and records have been completed to ensure safety and effectiveness of the device."

The overall "performance" is implicitly evaluated against the predicate device (AquariusAPS Server, K061214) by demonstrating substantial equivalence, meaning the device performs as intended and does not raise new safety or effectiveness concerns. The specific performance reported is the ability to identify a broader range of anatomical structures and support additional modalities compared to the predicate.

2. Sample Size for Test Set and Data Provenance

• Sample Size for Test Set: Not explicitly stated as a single number. The document mentions that for each modality, three databases were built (training, validation, and testing). The test databases "originated from a different healthcare system."
• Data Provenance:
  • Country of Origin: Not explicitly stated, but implies multiple centers/institutions. "27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets."
  • Retrospective or Prospective: Retrospective study.

3. Number of Experts and Qualifications for Ground Truth – Test Set

This information is not provided in the given document. The document states "A retrospective study was designed to assess the subject device accuracy," but does not detail how the ground truth for this test set was established, including the number or qualifications of experts.

4. Adjudication Method for Test Set

This information is not provided in the given document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not done based on the provided information. The study described focuses on the standalone performance of the AI algorithm.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The retrospective study was designed "to assess the subject device accuracy" focusing on the algorithm's ability to generate "anatomic descriptors." The "AI algorithm" component of the device "analyzes the CT or MR image pixel data and generates the anatomic descriptors."

7. Type of Ground Truth Used (for Test Set)

The specific type of ground truth (e.g., expert consensus, pathology, outcomes data) used for the test set is not explicitly stated. It is implied that for anatomical structure detection, the ground truth would be based on expert anatomical labeling or established anatomical atlases, but the document does not confirm this.

8. Sample Size for Training Set

The exact sample size for the training set is not explicitly stated. The document mentions that "For each modality, three databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region."

9. How Ground Truth for Training Set Was Established

This information is not explicitly stated in the provided document. It can be inferred that the "balanced distribution of studies per body region" implies these regions were labeled, but the methodology for establishing these labels (e.g., expert labeling, automated segmentation, etc.) is not described.

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Change Healthcare Enterprise Viewer is a software-based enterprise viewer intended to be used with off-the-shelf hardware for the 2D and 3D display of DICOM and non-DICOM medical images, reports, and multimedia content. Change Healthcare Enterprise Viewer is intended as well to facilitate collaboration and sharing of these materials within and outside the healthcare enterprise.

Change Healthcare Enterprise Viewer is intended to enable trained healthcare professionals to perform a diagnostic review on a workstation and non-diagnostic review on a mobile device, of a patient's medical images and to aid their clinical decision-making process.

Users access the application on a desktop computer or specific mobile devices through a standard web browser.

Change Healthcare Enterprise Viewer is not intended for diagnostic use on a mobile device. When used on a mobile device, Change Healthcare Enterprise Viewer is not intended to replace full workstations and should only be used when there is no access to a workstation.

Change Healthcare Enterprise Viewer is not intended for primary mammography diagnosis.

Change Healthcare Enterprise Viewer is an enterprise medical image viewer software application used with off-the-shelf servers, web browsers, and specific mobile devices for the 2D & 3D display of DICOM and non-DICOM medical images, reports, and multimedia content.

Change Healthcare Enterprise Viewer is intended to connect to an existing Picture Archiving and Communication System (PACS) or Vendor Neutral Archive (VNA) and is used to display medical images of multiple for clinical review, sharing and collaboration purposes.

The provided document is a 510(k) summary for the Change Healthcare Enterprise Viewer. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain information about acceptance criteria, detailed study designs, or performance metrics in the way requested in the prompt.

Specifically, the document states: "No clinical studies were necessary to support substantial equivalence." This means that the information requested regarding test sets, ground truth establishment, expert adjudication, MRMC studies, standalone performance, and detailed sample sizes for training/test sets is not present because such studies were not conducted or submitted for this particular 510(k) clearance. The clearance was based on demonstrating substantial equivalence through technological characteristics and intended use alignment with a predicate device, rather than a performance study meeting specific acceptance criteria.

Therefore, I cannot fulfill the request for a table of acceptance criteria and reported device performance, nor can I provide information about the study design elements such as sample sizes, expert qualifications, or adjudication methods, as this information is not included in the provided 510(k) summary.

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