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PAJUNK®'s Kit for balloon aided laparoscopy is intended for making incisions into the patients body to allow insertion of endoscopes and endoscopic accessories during general and minimal invasive surgical procedures.

The kit provides a common port with trocar and valve closure (for gaining acces for minimal invasive surgery) packed in a separate bag within the sterilized unit and a rigid balloon guidance with obturator and filling syringe (for insertion of the endoscopic visualisation device) packed in another separate bag. Both units are available seperately and as a procedure unit.

It is especially intended for diagnostics and aftercare in minimal invasive procedures.

Due to the length of the balloon quidance and the clinical practice there is no need to create a pneumoperitoneum via insufflation

The port may be left in place for up to 10 days with the seal closure trocar in place.

The kit is sterile and intended for single use.

The provided text describes a 510(k) premarket notification submission for PAJUNK®'s Kit for balloon aided laparoscopy. This submission is for a medical device, not an AI/ML powered device, and therefore does not contain information typically associated with AI/ML device studies and acceptance criteria.

The submission focuses on demonstrating substantial equivalence to predicate devices already cleared for market, rather than proving performance against specific acceptance criteria through clinical studies in the way an AI/ML device would.

Therefore, many of the requested sections about acceptance criteria, device performance, sample sizes, ground truth, expert involvement, and MRMC studies are not applicable to this document. The document describes a traditional medical device submission process for demonstrating safety and effectiveness based on equivalence to existing devices.

However, I can extract information related to the device's intended use, components, and the comparison to predicate devices, which forms the basis of its acceptance for market clearance.

Here's a breakdown of the relevant information from the document and why other sections are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: For this type of device (a Class II Endoscope and accessories), the "acceptance criteria" for 510(k) clearance are primarily centered around demonstrating substantial equivalence to legally marketed predicate devices in terms of:
  • Intended Use: The new device must have the same intended use as the predicate or a modified intended use that does not raise new questions of safety and effectiveness.
  • Technological Characteristics: The new device must have the same technological characteristics as the predicate, or different technological characteristics that do not raise new questions of safety and effectiveness and are supported by appropriate scientific testing.
  • Safety and Effectiveness: The device must be as safe and effective as the predicate device(s).
• Reported Device Performance: The submission asserts that the device is "substantially equivalent to the predicate devices and safe and effective" based on:
  • Comparison of technological characteristics.
  • Validated sterilization process.
  • Biocompatibility data (not detailed in this excerpt but mentioned).
  • The "optional use of optical devices (image giving endoscopes) in order to monitor the procedure and to conduct manually operated interventions under sight/ view are at least as safe and effective as common techniques are, actually this is intended to enhance safety and effectiveness."

Table format (interpreted for this traditional device):

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Not Applicable. This submission does not describe a clinical performance study with a "test set" in the context of an AI/ML device validating a diagnostic or predictive capability. The "study" here is primarily a comparative analysis against predicate devices and validation of established manufacturing processes (like sterilization), not a data-driven performance test set.

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

• Not Applicable. As above, there is no "ground truth" establishment in the context of a diagnostic or predictive algorithm. The device's safety and effectiveness are established through engineering principles, material science, equivalence to existing devices, and validated manufacturing processes, not through expert-labeled data for an algorithm.

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

• Not Applicable. There is no test set or adjudication process described for performance evaluation in the context of an AI/ML device.

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 device is an instrument for surgical procedures (kit for balloon aided laparoscopy), not an imaging analysis tool, diagnostic aid, or AI/ML product. Therefore, MRMC studies and "human readers improve with AI" metrics are irrelevant.

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

• Not Applicable. This is a hardware medical device, not an algorithm.

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

• Not Applicable. There is no ground truth data type as the device's function is mechanical/procedural, not data analysis or diagnosis. Safety and effectiveness are based on design, materials, manufacturing controls, and comparison to existing safe and effective devices.

8. The sample size for the training set:

• Not Applicable. There is no "training set" as this is not an AI/ML device.

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

• Not Applicable. There is no training set or ground truth in this context.

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