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The troCarWash™ system consists of a reusable control unit and a disposable, sterile, single-use trocar intended to remove visual obstructions such as condensation, blood, and other tissue particulates from the distal lens of a laparoscope during surgery and therefore maintain a clear image of the surgical site.

The troCarWash™ system is a laparoscopic lens cleaning device which is intended to be used during any laparoscopic surgical procedure where there is a potential for contamination of the laparoscope. Consisting of a mains powered reusable control unit, and a single use disposable trocar, obturator, and tubing set, it is intended to maintain surgical vision by removing contaminants such as condensation, blood, peritoneal fluid, smoke, fat, and tissue smears that have contaminated the distal lens of the laparoscope during surgical procedures providing a clear image of the The disposable portion of the system is sterilized via irradiation and has limited (<24 hours) patient contact with abdominal tissue. The trocar and obturator are primarily made of ABS and polycarbonate, and the tubing set is made of ABS, Polycarbonate, and PVC tubing. The system is intended for use in typical hospital environments by medical professionals and is suitable for all patients approved for laparoscopic operations. The system incorporates a software that initiates a short (<250 millisecond) wash and dry mechanism to efficiently clean the laparoscope and may be initiated with a natural single-handed surgical motion of retracting the scope momentarily into the supplied trocar.

The provided FDA 510(k) summary for the troCarWash™ System focuses on a device that removes visual obstructions from laparoscope lenses during surgery. The submission describes various non-clinical tests (packaging, sterilization, biocompatibility, electrical safety/EMC, software verification/validation, and bench performance testing) to demonstrate substantial equivalence to a predicate device.

However, the provided text does not contain the specific information required to answer many parts of your request, particularly regarding:

• Acceptance criteria values for performance (e.g., target accuracy, sensitivity, specificity, or specific cleaning efficacy metrics). The text states "the troCarWash™ system was able to achieve acceptable cleans" but doesn't quantify what "acceptable" means.
• Detailed study design for performance evaluation beyond "Bench Performance Testing." While it mentions "wash efficacy," it doesn't provide specific metrics.
• Sample size and data provenance for a "test set" in the context of AI/ML or comparative studies with human readers.
• Details on expert ground truth establishment, adjudication methods, or MRMC studies.
• Information about a training set for an AI/ML model or how its ground truth was established. This device is a mechanical cleaning system, not an AI/ML diagnostic or therapeutic device, so these concepts (training set, experts for ground truth) likely don't apply in the way you're asking.
• Standalone performance metrics (e.g., Sensitivity, Specificity, AUC) typical of AI/ML devices.

Based on the provided document, here's what can be inferred and what information is missing:

Device Description: The troCarWash™ system is a laparoscopic lens cleaning device consisting of a reusable control unit and a disposable, sterile, single-use trocar. It removes visual obstructions (condensation, blood, tissue particulates) from the distal lens of a laparoscope to maintain a clear image during surgery. It uses medical-grade CO2 and saline for cleaning.

Nature of Device: This is primarily a mechanical/electromechanical device, not an AI/ML-driven diagnostic or therapeutic device. Therefore, many of your questions related to AI/ML specific performance metrics (e.g., human reader improvement with AI assistance, training data, ground truth establishment by experts for AI models) do not directly apply to the described device and the information provided. The "Software Verification and Validation Testing" section explicitly states the software was considered "minor" level of concern, implying it's not performing complex diagnostic or decision-making functions typically associated with AI.

Table of Acceptance Criteria and Reported Device Performance

As the document does not specify quantitative acceptance criteria or performance metrics for wash efficacy (e.g., percentage of debris removed, clarity score), a table as requested cannot be fully populated. The closest statement is:

Study Information (Based on available text):

1. A table of acceptance criteria and the reported device performance:

   • Acceptance Criteria for Wash Efficacy: Not explicitly quantified in the provided text. It states "acceptable cleans."
   • Reported Device Performance for Wash Efficacy: "The study demonstrated that with various scope angles and brands, the troCarWash™ system was able to achieve acceptable cleans." (No quantitative metrics provided).

2. Sample sized used for the test set and the data provenance:

   • Sample Size: "29 disposable sets (trocar, obturator, and tubing set) preconditioned with sterilization and accelerated aging and 8 disposable sets preconditioned with sterilization and simulated transportation and distribution were tested for wash efficacy." This totals 37 disposable sets.
   • Data Provenance: The document does not specify the country of origin. The study was a "benchtop model" performance test, implying controlled laboratory conditions rather than retrospective or prospective clinical data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. This device is evaluated for its mechanical cleaning performance, not for diagnostic accuracy requiring expert interpretation or ground truth labeling in the context of an AI/ML model. The "acceptable cleans" would likely have been determined against pre-defined visual or technical criteria, not expert consensus on medical images.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable. This type of adjudication method is used in studies involving human interpretation (e.g., radiology reads) to resolve disagreements and establish ground truth for image-based diagnostic systems. It is not relevant for a mechanical cleaning efficacy test.

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 study was not done. The document explicitly states: "No clinical data was necessary to support a claim of substantial equivalence." This type of study is typically performed for AI/ML diagnostic aids to show improvement in human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • The "Bench Performance Testing" section describes testing the system's ability to clean without direct human intervention in the cleaning process itself (though human operation initiates the cycle). However, this is not "standalone algorithm performance" in the context of AI/ML, but rather the performance of the device's mechanical function. No specific quantitative diagnostic metrics (like sensitivity, specificity, AUC) are provided.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For wash efficacy: The ground truth would likely be defined by pre-established physical/visual criteria for lens cleanliness after contamination, rather than expert consensus on medical images or pathology. The document doesn't detail these criteria, only that "acceptable cleans" were achieved.
   • For other tests (packaging, sterilization, biocompatibility, electrical safety): Ground truth is established by compliance with recognized industry standards and test methods.

8. The sample size for the training set:

   • Not applicable / Not specified. This device is not an AI/ML model that requires a "training set" in the computational sense. Its design and operation are based on engineering principles, and its software is "minor" in terms of risk, suggesting it's primarily for control, not learning.

9. How the ground truth for the training set was established:

   • Not applicable. As no AI/ML training set is mentioned or implied, this question does not apply.

Summary of Gaps: The provided text is a 510(k) summary for a mechanical device, emphasizing its substantial equivalence through non-clinical performance and safety testing. It lacks the quantitative performance metrics, study design details, and specific AI/ML related information (training sets, expert ground truth, adjudication, MRMC studies) that your questions are designed to uncover for AI/ML medical devices.

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