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510(k) Data Aggregation
(180 days)
The Reprocessed Hot Biopsy Forceps are intended to be used for endoscopic tissue sample acquisition, controlled coagulation/cautery, and sessile polyp removal in the gastrointestinal tract.
The device consists of a proximal handle containing an electrosurgical plug which is connected, via a coil, spring or rod mechanism, to the biopsy cup or alligator forceps at the distal tip. The biopsy forceps range from 160 to 240 cm in length and 2.2-2.5 mm in diameter. The jaws range from 2.2 to 2.5 mm in diameter. The cup forceps may or may not have serrated edges, a fenestration, needle, or distal biting tooth. The forceps are covered with a sheath of electrical insulation. The electrosurgical plug on the handle used for connection to the appropriate electrosurgical unit. This submission does not include the electrosurgical box that the forceps connect to or its associated connecting cable.
The provided document describes a 510(k) summary for "Reprocessed Hot Biopsy Forceps" by SterilMed, Inc. It outlines the device's description, intended use, and the testing conducted to demonstrate substantial equivalence to predicate devices. However, the document does not contain information typically found in acceptance criteria tables or detailed study results for AI/ML device performance.
Specifically, the document focuses on regulatory approval for a reprocessed medical device, not an AI/ML device. Therefore, the requested information regarding acceptance criteria, device performance metrics (like sensitivity, specificity), sample sizes for test/training sets, ground truth establishment, expert qualifications, adjudication methods, or MRMC comparative effectiveness studies is absent from this regulatory submission.
The "Functional and Safety Testing" section vaguely states that "Representative samples of reprocessed hot biopsy forceps underwent bench testing to demonstrate appropriate functional characteristics." This is a high-level summary and does not provide specific performance metrics or detailed study designs.
Therefore, I cannot provide the requested information for an AI/ML device based on the given input.
Should the request pertain to the reprocessed hot biopsy forceps themselves (as a non-AI device), the relevant information from the document is summarized below, but it does not fit the typical format for AI/ML device performance evaluation criteria.
Based on the provided document, here's what can be extracted regarding the device and its testing, keeping in mind it's for a reprocessed physical medical device, not an AI/ML system:
1. A table of acceptance criteria and the reported device performance:
The document does not explicitly state quantitative "acceptance criteria" for performance metrics like sensitivity, specificity, or accuracy, as would be expected for an AI/ML device. Instead, it refers to "appropriate functional characteristics" for a reprocessed physical device.
| Acceptance Criteria (Implied for Reprocessed Hot Biopsy Forceps) | Reported Device Performance (as stated in the document) |
|---|---|
| Maintenance of appropriate functional characteristics | Representative samples underwent bench testing to demonstrate appropriate functional characteristics. |
| Validation of cleaning and sterilization procedures | Process validation testing was done to validate the cleaning and sterilization procedures. |
| Validation of device's packaging | Process validation testing was done to validate the device's packaging. |
| Visual and functional testing as part of manufacturing process | The manufacturing process includes visual and functional testing of all products produced. |
| Substantial Equivalence to Predicate Devices | Concluded to be substantially equivalent to four specific predicate devices based on similarities in functional design, materials, indications for use, and methods of construction. |
2. Sample size used for the test set and the data provenance:
- Sample Size for Test Set: The document states "Representative samples" but does not provide a specific number for the bench testing or process validation.
- Data Provenance: Not specified, but generally, such testing would be conducted in a controlled lab environment by the manufacturer (SterilMed, Inc., Minneapolis, MN, USA). The studies are inherently prospective in the sense that they are performed on the reprocessed devices prior to market release, but the data used to inform the reprocessing protocol might stem from retrospective analysis of failed devices or historical data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided. For a physical device, "ground truth" often refers to engineering specifications, industry standards, or performance benchmarks established at the time of original manufacturing. It's not typically established by human "experts" in the same way as an AI/ML diagnostic system.
4. Adjudication method for the test set:
Not applicable and not mentioned.
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. This is not an AI/ML device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. This is not an AI/ML device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
For a physical reprocessed device, the "ground truth" is implied to be:
- Engineering specifications of the original device.
- Performance benchmarks (e.g., electrical integrity, mechanical strength, sterility, cleanliness) that allow the device to function as intended and safely.
- Regulatory standards for reprocessing in terms of cleanliness, sterilization, and function.
8. The sample size for the training set:
Not applicable/not specified. There is no "training set" in the context of an AI/ML algorithm for this physical device.
9. How the ground truth for the training set was established:
Not applicable/not specified. There is no "training set" for an AI/ML algorithm.
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