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The NobleStich EL is indicated for use in the placement of sutures for soft tissue approximation in cardiovascular procedures. The NobleStitch EL is not intended for blind vascular closure.

The NobleStich EL device is a single-use surgical device, indicated for use in direct contact within the vascular and cardiovascular system. The NobleStitch EL device may be used by insertion through an access device (e.g. trocar, sheath or cannula), already in place following diagnostic or interventional procedures.

Based on the provided text, the device in question is the NobleStitch EL Suturing Device, a non-AI/ML device. The document is a 510(k) summary and FDA clearance letter, which focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting a performance study for an AI/ML diagnostic tool.

Therefore, the requested information regarding acceptance criteria, study design for AI/ML performance, ground truth establishment, expert adjudication, sample sizes for training/test sets, and MRMC studies cannot be extracted from this document as it pertains to a traditional medical device clearance process, not an AI/ML device approval.

The document discusses "Non-Clinical Test Summary" which details bench testing and clinical simulations, but these are for the physical device's functionality and performance, not for an AI/ML algorithm's diagnostic or predictive capabilities.

Summary of what can be extracted/inferred from the document regarding the device's testing, not related to AI/ML device performance:

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

• Acceptance Criteria (Implicit - demonstrated through testing): The device functions as intended and is as safe and effective as the predicate device (K113494).
• Reported Device Performance (as summarized):
  • Functional Tests: Passed visual inspection and operation.
  • Performance Tests: Actuation and verification of preloaded formed suture.
  • Clinical Simulation Tests: Actuated using a clinical simulation porcine model.

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

• Sample Size: Not specified in terms of numerical quantity for the tests. It mentions "a clinical simulation porcine model" but doesn't quantify how many uses or simulations were performed.
• Data Provenance: The "clinical simulation porcine model" suggests animal data, likely controlled laboratory conditions inherent to non-clinical bench testing. Country of origin not specified. These are non-clinical, likely prospective tests for the device's function.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• Not applicable / Not mentioned. This document describes testing for a physical suturing device, not an AI/ML algorithm requiring expert interpretation of medical images or data.

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

• Not applicable / Not mentioned. Adjudication methods are typically for resolving discrepancies in expert interpretations in AI/ML performance studies.

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 document is not about an AI/ML device requiring MRMC studies.

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

• Not applicable. This is not an AI/ML algorithm. The device itself is a standalone physical instrument.

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

• For the non-clinical tests, the "ground truth" would be the successful mechanical function of the device, proper suture deployment, and usability in the porcine model as defined by predetermined engineering and functional specifications. It's not a medical "diagnosis" ground truth.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that undergoes "training."

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

• Not applicable.

Conclusion: The provided text is a 510(k) clearance document for a physical medical device (suturing device), not an AI/ML diagnostic or therapeutic system. Therefore, the questions specifically pertaining to AI/ML device performance evaluation methods (e.g., training/test sets, expert adjudication, MRMC studies, ground truth for AI algorithms) are not addressed in this document. The "Non-Clinical Test Summary" describes traditional bench and simulation testing for the physical device's functionality.

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