Hi All,
We have a software intended for use in post-processing of MR images to do image segmentations, and provide the measurements derived from these segmentations. For our performance testing, the company wants to evaluate our output against the predicate device(s) output, ie, using the predicate device as the gold standard. I am not a fan of this approach, as the predicate device may have had an acceptance criteria for their output against manual segmentation (usual gold standard), and if we have an acceptance criteria against the predicate device's output; the output of our software may be some ways off of manual segmentation, which I think the FDA will frown on.
Has anyone had any experience in this field and how did you resolve it?
For performance testing, is it enough to prove that the performance of the software product is "as good as" the predicate device? The evaluating substantial equivalence (sorry, can't post links yet) guidance document does not provide an answer for or against.
Thanks!
We have a software intended for use in post-processing of MR images to do image segmentations, and provide the measurements derived from these segmentations. For our performance testing, the company wants to evaluate our output against the predicate device(s) output, ie, using the predicate device as the gold standard. I am not a fan of this approach, as the predicate device may have had an acceptance criteria for their output against manual segmentation (usual gold standard), and if we have an acceptance criteria against the predicate device's output; the output of our software may be some ways off of manual segmentation, which I think the FDA will frown on.
Has anyone had any experience in this field and how did you resolve it?
For performance testing, is it enough to prove that the performance of the software product is "as good as" the predicate device? The evaluating substantial equivalence (sorry, can't post links yet) guidance document does not provide an answer for or against.
Thanks!