(26 days)
Workflow Box is a software system designed to allow users to route DICOM-compliant data to and from automated processing components. Supported modalities include CT, MR. RTSTRUCT
Workflow Box includes processing components for automatically contouring imaging data using deformable image registration to support atlas based contouring of the same patient and machine learning based contouring.
Workflow Box is a data routing and image processing tool which automatically applies contours to data which is sent to one or more of the included image processing workflows. Contours generated by Workflow Box may be used as an input to clinical workflows including, but not limited to, radiation therapy treatment planning.
Workflow Box must be used in conjunction with appropriate software to review and edit results generated automatically by Workflow Box components, for example image visualization software must be used to facilitate the review and edit of contours generated by Workflow Box component applications.
Workflow Box is intended to be used by trained medical professionals.
Workflow Box is not intended to automatically detect lesions.
Workflow Box is a software application that enables the routing of image data and structures to automatic image processing workflows, including atlas based contouring, image registration based re-contouring and machine learning based contouring.
Workflow Box data routing and contouring workflows support CT, MR and RTSTRUCT image data and structures. Workflow Box supports the routing of data to and from DICOM nodes within a hospital network.
Once data is routed to the auto contouring workflows there is no user interaction required and no user interface for visualizing image data. The configuration of workflows and data routing rules are managed via an administration interface.
Workflow Box must be used in conjunction with appropriate software to review and edit results generated automatically by Workflow Box components. Image visualization software, such as a treatment planning system, must be used to facilitate the review and edit of contours generated by Workflow Box component applications.
The provided text is a 510(k) Summary of Safety and Effectiveness for Mirada Medical Ltd.'s "Workflow Box" device. It describes the device, its intended use, and compares it to a predicate device (Mirada RTx). However, it does not contain the specific details about acceptance criteria, device performance, study design, ground truth establishment, or sample sizes as requested in the prompt.
The document states that "The results of performance, functional and algorithmic testing demonstrate that Workflow Box meets the user needs and requirements of the device," but it does not present these results or the criteria for acceptance.
Therefore, I cannot populate the table or answer most of the questions because the information is not present in the provided text.
Here's what I can extract and what is missing:
Information Present:
- Device Name: Workflow Box
- Predicate Device: Mirada RTx (K130393)
- Modality: CT, MR, RTSTRUCT
- Functionality: Data routing, automatic contouring using deformable image registration for atlas-based contouring, and machine learning-based contouring.
- Operating System: Microsoft Windows 10 (64-bit) and Microsoft Windows Server 2016.
- Validation Statement: "Workflow Box is validated and verified against its user needs and intended use by the successful execution of planned performance, functional and algorithmic testing included in this submission. The results of performance, functional and algorithmic testing demonstrate that Workflow Box meets the user needs and requirements of the device, which are demonstrated to be substantially equivalent to those of the listed predicate device."
- Safety Statement: "Workflow Box meets requirements for safety and effectiveness and does not introduce any new potential safety risks."
Missing Information (and thus cannot create the table or answer the specific questions):
- Acceptance Criteria (quantifiable metrics): The document states testing was done, but no specific performance targets or thresholds are listed.
- Reported Device Performance: No actual performance metrics (e.g., Dice similarity coefficient, mean distance to agreement, sensitivity, specificity) are reported.
- Sample Size (Test Set): Not mentioned.
- Data Provenance (Test Set): Not mentioned (e.g., country, retrospective/prospective).
- Number of Experts for Ground Truth (Test Set): Not mentioned.
- Qualifications of Experts: Not mentioned.
- Adjudication Method (Test Set): Not mentioned.
- Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not mentioned as being performed or its results.
- Effect size of human readers with/without AI assistance: Not mentioned.
- Standalone Performance Study: The document indirectly implies standalone performance by stating the device "automatically applies contours" and "processing components for automatically contouring." However, no specific study details are provided.
- Type of Ground Truth: Not explicitly stated how the "ground truth" for contouring was established (e.g., expert consensus, pathology).
- Sample Size (Training Set): Not mentioned.
- Ground Truth for Training Set Establishment: Not mentioned.
Conclusion:
Based on the provided text, a comprehensive answer to your request cannot be given as the detailed study findings, acceptance criteria, and specific performance metrics are not included in this 510(k) summary. The document asserts that testing was performed and demonstrates substantial equivalence, but it does not provide the granular data you are seeking.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).