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The Dexterity® Pneumo Sleeve is intended to provide abdominal access for the surgeon's hand while preserving pneumoperitoneum during laparoscopic surgery.

The Protector® Retractor is intended to provide incision retraction and to protect it against wound contamination during both laparoscopic and open surgery.

This device allows the surgeon to use his hand while performing corrective surgery in the abdominal cavity under pneumoperitoneum.

The Dexterity® Pneumo Sleeve is a sterile, disposable device. It prevents leakage of CO2 and provides an extension of the pneumoperitoneum. It has an adhesive flange at one end that is placed over the inoision and adhered to the patient's abdomen. A velcro strap at the other end is used to tighten the Sleeve around the surgeon's arm.

The Protector® Retractor is a sterile, disposable device. The Retractor consists of an open-ended cylinder with a flexible ring at each end. One ring is inserted through the incision into the peritoneal cavity while the other remains outside the incision. The Retractor holds the incision open and protects it against wound contamination.

Here's an analysis of the provided text regarding acceptance criteria and the supporting study, structured according to your request.

It's important to note that the provided 510(k) summary (K962147 for the Dexterity® Pneumo Sleeve and Protector® Retractor) describes a medical device for surgical access, not an AI/ML powered device. As such, many of your requested criteria, which are highly specific to AI/ML device validation (e.g., sample size for test set/training set, expert grounding, MRMC studies, standalone performance, data provenance for AI), are not applicable to this traditional medical device submission. I will answer those questions with "Not Applicable" and explain why.

K962147: Dexterity® Pneumo Sleeve and Protector® Retractor

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly list quantitative "acceptance criteria" in the way one might for an AI algorithm (e.g., target specificity, sensitivity, or AUC). Instead, the "acceptance criteria" for this traditional medical device are defined by its ability to demonstrate safety and effectiveness for its intended use, typically by being substantially equivalent to a predicate device and showing favorable outcomes in a clinical setting against established surgical practices.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set:
  • Pilot Study: 10 surgeons and 40 patients.
  • Pivotal Clinical Study: Not explicitly stated, but was a "prospective, randomized, controlled trial" across 10 clinical centers. The exact number of patients or surgeons is not provided in this summary.
• Data Provenance: The pivotal clinical study was conducted at "10 clinical centers in the United States and Europe." This indicates a prospective collection of clinical data from these regions.

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

• Not Applicable. This is a device for surgical access, not an AI algorithm that requires expert annotation for "ground truth" establishment in a diagnostic or interpretive sense. The "ground truth" for this device's performance is derived from clinical outcomes and surgeon's ability to perform the procedure as intended. The "experts" observing the performance would be the surgeons and clinical staff involved in the studies, assessing patient outcomes and device function.

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

• Not Applicable. Adjudication methods like 2+1 or 3+1 are typically used for resolving discrepancies in expert interpretations (e.g., image readings) to establish a definitive ground truth for AI model training or evaluation. This concept does not apply to the evaluation of a surgical access device. The clinical study would have had its own statistical analysis plan and potentially an independent data monitoring committee, but not expert adjudication in this sense.

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. MRMC studies are specific to evaluating diagnostic AI systems and their impact on human reader performance. This device is a surgical instrument and has no "readers" or "AI assistance" component in that context. The pivotal clinical study was a "randomized, controlled trial" comparing the device-assisted approach to "routine laparoscopic surgery," which is a comparative effectiveness study, but not of the MRMC type.

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

• Not Applicable. This is a physical surgical device, not an algorithm. Standalone performance is a concept for AI models.

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

• The "ground truth" for this device's efficacy and safety was established by clinical outcomes data from the randomized controlled trial. This includes metrics like overall procedure time, number of instrument changes, conversion rates, direct operation costs, time to return to normal activity, and morbidity and mortality. The "operating principles" were initially validated by direct observation and feedback from surgeons in the pilot study.

8. The sample size for the training set

• Not Applicable. This summary describes a traditional hardware medical device, not an AI/ML algorithm that requires a "training set."

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

• Not Applicable. As there is no AI/ML training set, there's no ground truth to establish for it.

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