(58 days)
The Imbio Segmentation Editing Tool Software is used by trained medical professionals asa tool to modify the contours of segmentation masks produced by Imbio algorithms or to manually create segmentation mask contours. The Segmentation Editing Tool can provide further support to the users of Imbio's algorithms.
Imbio Segmentation Editing Tool (SET) Software is a segmentation editing tool designed to allow users to optimize segmentations calculated by Imbio's fully-automated suite of algorithms (Each algorithm is a separate Imbio program and either has or will be submitted for regulatory approval independently). Imbio is building a suite of medical image post-processing applications that run automatically after data transfer off the medical imaging scanner. Automatic image segmentation is often an essential step in Imbio's analyses. To date, the automatic segmentation algorithms used in Imbio's applications have been robust, however segmentation failures do occur. The purpose of the Segmentation Editing Tool is to provide customers with a tool to locally correct poor segmentations. Additionally, if the Imbio automatic segmentation fails such that it is unable to produce a result, this tool can be used to semi-manually draw the segmentation required for analysis. SET reads in anatomical images used in an automatic segmentation algorithm and the results of the automated segmentation algorithm (if available). The user is then able to locally correct insufficiencies in the segmentation result, or create a segmentation mask from scratch. The finalized segmentation mask is then pushed back to Imbio's Core Computing Platform and the job is re-processed.
The provided FDA 510(k) summary for the "Imbio Segmentation Editing Tool Software" (K180129) does not contain details about specific acceptance criteria or a study that proves the device meets such criteria.
Instead, it focuses on demonstrating substantial equivalence to a predicate device (MIM 5.2 Brachy K103576). The document mentions "Non-clinical testing was done to show validity of SET software" and "Design validation was performed using the Imbio Segmentation Editing Tool Software in actual and simulated use settings," but it does not provide the results of these tests, specific acceptance criteria, or detailed methodologies.
Here's a breakdown based on the information provided and not provided in the document:
1. A table of acceptance criteria and the reported device performance:
- Not provided. The document does not list any specific quantitative acceptance criteria (e.g., Dice score, mean surface distance, sensitivity, specificity) or present device performance metrics against such criteria.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
- Not provided. The document states "Design validation was performed using the Imbio Segmentation Editing Tool Software in actual and simulated use settings," but it does not specify the number of cases (sample size) used for these tests, nor the origin or nature (retrospective/prospective) of the data.
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. Since specific test sets and ground truth establishment are not detailed, this information is absent. The device is a segmentation editing tool meant to be used by "trained medical professionals," suggesting a human-in-the-loop context, but not a fully automated algorithm that produces its own segmentations against a pre-established ground truth for validation.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not provided. This information is typically relevant for studies where multiple readers determine ground truth or interpret results, which is not described for this device's validation.
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:
- Not provided. The document states, "This technology is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."
- While "Usability testing was completed for this product to ensure proper use of the product by intended users," this is not equivalent to an MRMC comparative effectiveness study measuring improved human performance with AI assistance. The device is a tool to edit segmentations, not an AI that provides initial interpretations.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not explicitly stated as a primary validation method for this specific 510(k). The device is described as "a segmentation editing tool designed to allow users to optimize segmentations calculated by Imbio's fully-automated suite of algorithms." It's an editing tool for other Imbio algorithms' outputs. Therefore, its performance is inherently linked to human interaction. The validation focuses on the tool's functionality for editing, not on a standalone algorithmic diagnostic performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not provided for the "non-clinical testing" or "design validation." Since the device is an editing tool, the "ground truth" in operational use would be the expert's final, corrected segmentation. How the accuracy of the editing tool itself was measured against a truth standard is not detailed.
8. The sample size for the training set:
- Not applicable / Not provided. The Imbio Segmentation Editing Tool Software is described as a software tool for editing segmentation masks, not an AI algorithm that learns from a training set to produce segmentations itself. The document mentions "Imbio's fully-automated suite of algorithms" which do perform segmentation, but this 510(k) is for the editing tool, not for those underlying algorithms. Therefore, a training set for the editing tool is not relevant in the same way it would be for a segmentation algorithm.
9. How the ground truth for the training set was established:
- Not applicable / Not provided. As mentioned above, the editing tool itself does not have a training set in the context of learning to perform segmentation.
§ 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).