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510(k) Data Aggregation
(282 days)
Gelseal Vascular Grafts, Gelsoft Vascular Grafts, Gelsoft Plus Vascular Grafts
Gelseal™ Vascular Prostheses: "Indicated for replacement or bypass of abdominal arteries afflicted with aneurysmal or occlusive disease."
Gelsoft™ Vascular Prostheses: "Indicated for abdominal and peripheral vascular repair, i.e. replacement or bypass in aneurysmal and occlusive disease of arteries."
Gelsoft™ Plus Vascular Prostheses: "Indicated exclusively for vascular repair of damaged and diseased vessels of the abdomen, i.e. replacement or bypass in aneurysmal and occlusive disease of abdominal arteries."
Gelseal. Gelsoft and Gelsoft Plus vascular prostheses are gelatin sealed knitted polyester prostheses, designed for vascular repair.
The Vascutek Gelseal, Gelsoft and Gelsoft Plus polyester vascular prosthesis family, which is the subject of this pre-market notification, is based on knitted polyester textile technology.
This is a medical device submission for a vascular graft. The document is a 510(k) summary and FDA clearance letter. It does not describe a study involving an algorithm or AI. Instead, it describes a device (vascular grafts) and its equivalence to predicate devices based on non-clinical testing.
Therefore, many of the requested categories related to algorithm performance, ground truth, expert review, and sample sizes for AI/algorithm studies are not applicable.
Here's the information that can be extracted, with notes on what is not applicable:
1. A table of acceptance criteria and the reported device performance
The document states: "The nonclinical testing performed, including physical and biocompatibility testing, chemical characterisation and an animal performance study, have demonstrated that the device is substantially equivalent the predicate devices."
However, specific acceptance criteria and reported numerical performance metrics for these nonclinical tests are not detailed within this document. The document primarily focuses on establishing substantial equivalence to predicate devices for regulatory clearance rather than presenting detailed test results.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: Not specified in this document for the nonclinical tests.
- Data Provenance: Not specified, but the tests were performed by the manufacturer, Vascutek Ltd., which is based in Scotland, UK. The studies would have been prospective to generate the data for this submission.
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 applicable. This device is a vascular graft, not an AI/algorithm-based diagnostic or prognostic tool requiring expert review of data for ground truth establishment.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This is not an AI/algorithm study requiring adjudication of expert interpretations.
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 applicable. No AI component is described.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. No algorithm is described.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable. For the nonclinical tests mentioned (physical, biocompatibility, chemical characterisation, animal performance study), the "ground truth" would be established by the defined test methodologies and objective measurements for each test, compared against predetermined specifications or predicate device performance. For example, for biocompatibility, it would be compliance with ISO standards (e.g., ISO 10993).
8. The sample size for the training set
Not applicable. There is no algorithm or AI model requiring a training set.
9. How the ground truth for the training set was established
Not applicable. There is no algorithm or AI model requiring a training set.
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