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510(k) Data Aggregation
(28 days)
Laparoscope Lens Shield Device (LENS), a sterile, single-use and disposable laparoscopic accessory lens shield device, for various sizes of laparoscopes including standard and bariatric laparoscope, intended to maintain the intra-operative view of the surgical site during minimally invasive surgery by physically shielding the laparoscope lens from debris, grease, blood, and bodily fluids.
The Laparoscope Lens Shield Device (LENS) is a laparoscopic accessory lens shielding device consisting of multi-lumen sheath that slides over the laparoscope. The sheath assembling consists of 2 concentric sheaths: one outer and one end-to-end connected inner sheaths. The outer sheath provides protection and cover for the inner sheath and shielding film. It is intended to maintain the intra-operative view of the surgical site during minimally invasive surgery by physically shielding the laparoscope lens from debris, grease, blood, and bodily fluids.
This document is a 510(k) Premarket Notification for a Laparoscope Lens Shield Device (LENS) for which the device is a laparoscopic accessory lens shield device to maintain the intra-operative view of the surgical site during minimally invasive surgery by physically shielding the laparoscope lens from debris, grease, blood, and bodily fluids.
The document describes modifications to a previously cleared predicate device (K170103) to accommodate laparoscopes with a 5mm outer diameter (the previous model accommodated 10mm). The submission is a "Special 510(k) Notification," indicating that the modifications do not raise new questions of safety or effectiveness. As such, the performance testing focuses on demonstrating that these modifications maintain substantial equivalence to the predicate device.
Here's an analysis of the provided information concerning acceptance criteria and supporting studies:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of numerical acceptance criteria with corresponding performance metrics for the modified device in the manner typically seen for diagnostic AI/ML devices (e.g., sensitivity, specificity thresholds). Instead, the acceptance criteria are implicitly met by demonstrating that the modified device performs similarly to the predicate device and meets established medical device standards.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Biocompatibility: Pass relevant tests for tissue contact, to ensure no adverse biological reactions.Mechanical Function and Structural Integrity: Design specifications are fulfilled, and safety/function are not affected by modification. | Biocompatibility: Passed cytotoxicity, sensitization, irritation, acute systemic toxicity, and pyrogenicity tests in accordance with ISO 10993-5, 10993-10, 10993-11, and USP Chapter <151>.Mechanical Testing: Demonstrated that design specifications are fulfilled and modifications do not affect safety and function. |
| Functional Performance (Maintain Intra-operative View): Intended use is fulfilled post-modification. | Functional Testing: Demonstrated that the intended use is fulfilled in an animal model, and design modifications do not affect the function and intended use of the device. |
| Substantial Equivalence to Predicate: No additional or different questions of safety or effectiveness are raised compared to the predicate device (K170103). | Demonstrated through comparison of intended use, technological characteristics, and performance testing, confirming no new safety or effectiveness concerns. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified in terms of number of cases or specific specimens.
- Data Provenance:
- Biocompatibility: Tested materials (implicitly, the materials used in the modified device). No geographic provenance for materials is given.
- Mechanical Testing: Performed on the modified device itself.
- Functional Testing: Performed in an animal model. No specific details on the animal model (e.g., species, number of subjects) or geographic origin are provided.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Not Applicable. This device is a physical medical device (a lens shield), not a diagnostic AI/ML algorithm that requires expert-established ground truth for its performance evaluation (e.g., for image interpretation). The evaluation focuses on physical, chemical, and biological performance characteristics, verified through established testing protocols and animal studies.
4. Adjudication Method for the Test Set
- Not Applicable. As noted above, this is a physical device, and its performance evaluation does not involve diagnostic interpretation or adjudication typically associated with AI/ML systems where ground truth is derived from human expert consensus.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No such study was done. This is a physical accessory device, not a diagnostic AI/ML algorithm that assists human readers in interpreting medical images. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
- Not Applicable. This is a physical medical device, not an algorithm. Its function is to physically shield a laparoscope lens, not to perform a standalone diagnostic interpretation.
7. Type of Ground Truth Used
- Biocompatibility: Ground truth is established by meeting the specific pass/fail criteria outlined in the referenced ISO and USP standards (e.g., no cytotoxicity, no irritation, no systemic toxicity above thresholds).
- Mechanical Testing: Ground truth is established by verifying that the device meets its design specifications and maintains structural integrity under tested conditions.
- Functional Testing: Ground truth is established by observing that the device fulfills its intended use (maintaining the intra-operative view by shielding the lens) in the animal model. This is an observational functional assessment rather than a diagnostic "ground truth."
8. Sample Size for the Training Set
- Not Applicable. This is a physical medical device. It does not involve machine learning or AI, and therefore does not have a "training set" in the computational sense.
9. How the Ground Truth for the Training Set Was Established
- Not Applicable. As there is no training set for an AI/ML algorithm, this question is not relevant.
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