(98 days)
The EnSite Verismo™ Segmentation Tool (EnSite Verismo) (EV1000) is indicated for use in generating 3D models from CT, MR or rotational angiography DICOM image data. Generated models are intended to be displayed on the EnSite Velocity™ System.
Not Found
The provided text is a 510(k) summary for the EnSite Verismo Segmentation Software v.2.0, focusing on its substantial equivalence to a predicate device. It does not contain information about specific acceptance criteria or a detailed study proving the device meets those criteria, as typically found in a clinical study report. Therefore, I cannot complete the table or answer most of the questions based on the provided text.
Specifically, the document states:
- "Bench testing was performed to confirm that the changes met design requirements and did not affect the safe or effective use of the product."
- "Where operational and performance differences exist between the proposed device and the predicate device, performance testing demonstrated that these differences do not adversely affect the device's safety and effectiveness."
This indicates that internal design requirements and performance testing were conducted, but the details of these are not disclosed in this summary.
Here's a breakdown of what can and cannot be extracted from the provided text:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not provided. The document states "bench testing was performed to confirm that the changes met design requirements," but does not list these specific requirements or their associated performance metrics. | Not provided. The document states "performance testing demonstrated that these differences do not adversely affect the device's safety and effectiveness," but no specific performance data (e.g., accuracy, precision) is given. |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample size for test set: Not provided.
- Data provenance: Not provided.
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 provided. The document does not describe any human expert review or ground truth establishment relevant to an external test set. It mentions "user testing" but no details are given.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not provided.
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
- Was an MRMC study done? Not provided. The focus is on substantial equivalence to a predicate device, not on reader improvement.
- Effect size: Not applicable/Not provided.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- The document implies standalone performance was assessed as part of "bench testing" and "performance testing" to ensure it met design requirements and was substantially equivalent, but no specific standalone performance metrics are given.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
- Not provided.
8. The sample size for the training set
- Not provided. The document doesn't explicitly mention training or validation sets, as its focus is on demonstrating substantial equivalence to a previous version through verification and validation activities.
9. How the ground truth for the training set was established
- Not provided.
§ 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).