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510(k) Data Aggregation
(58 days)
OnQ rts software is stand-alone software that provides a means of visualizing and comparing medical image data from multiple DICOM compliant imaging modality devices. It is used for the display, evaluation, co-registration and fusion of medical images, contour of anatomical structures and radiation therapy dose distributions to aid in radiation therapy planning, diagnostic radiology, oncology and other medical specialties. Note: The software is not for use with digital mammography
OnQ rts® is a stand-alone medical imaging software program that imports DICOM images from different modalities and provides imaging tools to visualise, compare, contour, co-register medical images, anatomical structures and radiation therapy dose distributions. These procedures are performed by manual, semi-automatic techniques which extract anatomical information for image contouring and analysis from the DICOM data. The system can operate as a single workstation or with multiple workstations, with one of them being the server networked with multiple clients and remotely via Citrix® terminal services. OnQrts is not intended for use with hand-held mobile devices.
OnQ rts software includes the import and export of DICOM data securely via a network connection. The imported images, from different modalities, are processed to provide a clinical picture of the anatomy and corresponding radiation therapy doses. OnQrts co-registers images together, using rigid image registration (RIR) and elastic/deformable image registration (DIR) or a combination of both. These images can display imported or generated contours (anatomical and dose) overlaid onto the fused images.
OnQ rts contouring can be performed manually or .automatically from a library of user defined or prepared pre-contoured CT cases. The library atlas files act as a template that is then mapped to the patients' anatomy. The contours are reviewed and approved before export, e.g. to radiotherapy planning software. OnQrts can import radiation therapy treatment planning data (dose and dose distributions) which can then be displayed for review, radiation therapy dose comparison and analysis. The software provides contour companson tools using a range of comparison metrics that highlight variations between VOIs in terms of size, shape and location. OnQ rts is not a radiation therapy treatment planning system and can only display and evaluate dose plan information generated from radiation therapy equipment.
The provided text describes the 510(k) summary for the OnQ rts® imaging software. Here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The submission does not specify quantitative acceptance criteria or a dedicated performance study with specific metrics like sensitivity, specificity, accuracy, or dice scores. Instead, the "acceptance criteria" appear to be met through demonstrating substantial equivalence to predicate devices via non-clinical testing.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Meets product requirements (features and technical characteristics equivalent to predicate devices) | "Test Plans were written and executed internally which validate that OnQ rts meets the product requirements. The product requirements include equivalent features and technical characteristics as the predicate device and the test results confirmed that OnQ rts is substantially equivalent to the predicate device." |
| Software performs appropriately | "These results concluded that the software performed appropriately and the testing included confirmation of image fusion, atlas based segmentation, auto contouring, 4D contouring, analysis tools (DVH), adaptive re-planning (dose mapping), integration and display of radiation therapy doses." |
| As safe, as effective, and performs as well or better than predicate devices | "This concludes that OnQ rts is as safe, as effective and performs as well or better than the predicate device." and "Based on the defined technical characteristics and the non-clinical testing that was performed it is determined that OnQ rts is as safe, as effective and performs as well or better than the predicate device and is therefore substantially equivalent." |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a sample size for a test set in the context of clinical performance evaluation. The "test plans" mentioned are for internal non-clinical validation. No patient data provenance (country of origin, retrospective/prospective) is provided, as no clinical testing was performed for substantial equivalence.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable. No clinical test set with expert-established ground truth was reported.
4. Adjudication Method for the Test Set
Not applicable. No clinical test set with adjudication was reported.
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 reported. This device is described as a standalone imaging software with manual/semi-automatic contouring capabilities, not explicitly an AI-assisted tool in the context of improving human reader performance.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document focuses on the software's functionality as a standalone tool that aids in clinical tasks. It mentions "auto contouring" and "atlas based segmentation" which implies algorithmic components, but no performance metrics for these features in isolation are provided in the summary. The overall evaluation is for the software system, not solely an algorithm's standalone diagnostic performance against a ground truth.
7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)
For the non-clinical testing, the "ground truth" was implicitly based on the expected behavior and accuracy of the implemented features, likely compared against predefined computational or functional specifications rather than clinical ground truth (e.g., pathology, expert consensus on patient images).
8. The Sample Size for the Training Set
Not applicable. The document does not describe a training set for a machine learning model in the conventional sense. The "library atlas files" mentioned for auto contouring act as a template, but the size or nature of this "training" data isn't specified in terms of machine learning training.
9. How the Ground Truth for the Training Set was Established
Not applicable. No specific training set for a machine learning model is described in the provided text.
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