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510(k) Data Aggregation
(189 days)
Reprocessed Stone Retrieval Baskets are used to entrap and remove renal stones and calculi via a rigid or flexible endoscope during transurethral or fluoroscopic percutaneous urologic procedures.
SterilMed's reprocessed stone retrieval baskets consist of a handle, shaft/sleeve, and a wire basket which serves as the stone capturing mechanism. They come in diameters ranging from 1.9 French to 8.0 French, and lengths ranging from 70 cm to 220 cm. The baskets are available in 3 to 8 wire configurations and flat or helical wire designs.
The provided document describes the 510(k) premarket notification for SterilMed's Reprocessed Stone Retrieval Baskets. This is a medical device submission for reprocessing existing devices, not a new AI/ML-driven device. Therefore, many of the requested categories in the prompt, such as acceptance criteria based on performance metrics (like sensitivity, specificity), sample sizes for test/training sets, expert ground truth establishment, MRMC studies, or standalone algorithm performance, are not applicable here.
The "device" in this context is the reprocessed version of existing stone retrieval baskets, and the study focuses on validating the reprocessing methods to ensure the reprocessed devices are substantially equivalent to new, predicate devices.
Here's the information that can be extracted from the provided text, framed within the context of a medical device submission for reprocessing:
Acceptance Criteria and Study for SterilMed's Reprocessed Stone Retrieval Baskets
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria Category | Description of Criterion | Reported Device Performance / Evidence of Meeting Criterion |
---|---|---|
Functional Characteristics | Device performs as intended (e.g., stone entrapment and removal). | "Representative samples of reprocessed stone retrieval baskets underwent bench testing to demonstrate appropriate functional characteristics." |
The FDA's substantial equivalence determination implies that the reprocessed devices perform functionally similar to the predicate devices. |
| Cleaning Validation | Reprocessing procedures effectively clean the device to remove biological and other contaminants. | "Process validation testing was done to validate the cleaning [...] procedures." |
| Sterilization Validation | Reprocessing procedures effectively sterilize the device, rendering it free of viable microorganisms. | "Process validation testing was done to validate the [...] sterilization procedures." |
| Packaging Validation | Repackaged device maintains sterility and integrity until point of use. | "Process validation testing was done to validate [...] the device's packaging." |
| Visual and Functional Inspection | Each reprocessed device meets quality standards before release. | "In addition, the manufacturing process includes visual and functional testing of all products produced." |
| Substantial Equivalence (Overall) | The reprocessed device is as safe and effective as a legally marketed predicate device. | "SterilMed's Reprocessed Stone retrieval baskets are substantially equivalent to: Boston Scientific Corporation's Stone Dislodger Baskets (K970121, K951309, K936721), Wilson-Cook Medical Inc.'s Stone Extractor (K851965), The counterpart devices from the original manufacturers."
This conclusion is based on "...these devices' are essentially identical to the predicate devices in terms of functional design, materials, indications for use, and principles of operation." |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: The document states "Representative samples" were used for bench testing and process validation. A specific numerical sample size is not provided.
- Data Provenance: The document does not specify the country of origin for the data or whether it was retrospective or prospective. Given it's bench testing and process validation, it's likely internal laboratory testing conducted by SterilMed, Inc.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This concept is not applicable to this type of submission. "Ground truth" for this device relates to objective physical and biological properties (e.g., cleanliness, sterility, mechanical function) rather than expert interpretation of data. The validation work would be conducted by qualified engineers and microbiologists, but they are not "experts establishing ground truth" in the same way as, for example, radiologists interpreting images.
4. Adjudication method for the test set
- This concept is not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements in expert interpretations or annotations, typically in fields like medical imaging. The tests performed here (bench testing for function, process validation for cleaning/sterilization) involve objective measurements and established protocols, not subjective expert judgment requiring adjudication.
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, an MRMC comparative effectiveness study was not done. This is a reprocessed medical device, not an AI/ML diagnostic or assistive tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- No, a standalone algorithm performance study was not done. This device does not involve an algorithm.
7. The type of ground truth used
- The "ground truth" here is based on objective measurements and validated protocols demonstrating device function, cleanliness, sterility, and packaging integrity. This is derived from:
- Bench test results (e.g., mechanical performance specifications).
- Microbiological testing results (e.g., sterility assurance levels, residual protein/bioburden).
- Packaging integrity tests.
- Visual and functional inspection criteria.
8. The sample size for the training set
- This concept is not applicable. There is no "training set" as this is not an AI/ML algorithm.
9. How the ground truth for the training set was established
- This concept is not applicable. As there is no training set for an algorithm, there is no ground truth established for it.
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