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The EndoFix™Tissue Fixation System is an endoscopic tissue approximation system intended for anchoring the gallbladder to the stomach or duodenum to aid placement of a luminal apposing metal stent (LAMS) for EUS-guided gallbladder drainage.

The EndoFix™ Tissue Fixation System (TFS) is an endoscopic (through the scope) tissue approximation device consisting of two main components:

1. EndoFix Tissue Fixation Device – Enables the approximation of soft tissue in the gastrointestinal (GI) tract using a suture-based implant delivered through an 18 gauge echogenic needle.
2. Secure Suture Locking Device – Secures the implant assembly with an implant grade suture lock and cuts the suture once approximation is complete.

The EndoFix TFS is designed to place an implant for endoscopic tissue approximation to anchor the gallbladder to the stomach or duodenum to aid placement of a luminal metal apposing stent (LAMS). During use, the EndoFix Tissue Fixation Device is advanced through a ≥3.2 mm working channel of a commercially available echoendoscope (EUS). Under ultrasound guidance, the EndoFix TFS needle is advanced through the target tissues and the implant subassembly is deployed to approximate the luminal tissues (either transgastric or transduodenal based on clinical requirements). With applied suture tension, a Suture Lock is deployed to secure the tissues using the Secure Suture Locking Device. The implant subassembly and Suture Lock are considered permanent implants.

Each of the respective delivery systems used to deliver the implant assembly/Suture Lock are short term contact devices.

This FDA 510(k) clearance letter for the EndoFix™ Tissue Fixation System does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a study proving those criteria are met. This document primarily focuses on establishing substantial equivalence to predicate devices for regulatory clearance.

Specifically, it does not include:

• A table of acceptance criteria with reported device performance metrics (e.g., success rates, tensile strength, specific measurements).
• Details about a "test set" for a performance study (sample size, data provenance, ground truth establishment, expert qualifications, or adjudication methods).
• Information on a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size for human reader improvement.
• Distinction between standalone algorithm performance and human-in-the-loop performance, as this is a medical device, not an AI/software-only device in the context of typical AI performance criteria.
• Training set sample size or how its ground truth was established.

However, based on the provided text, I can infer and extract some relevant information regarding performance testing.

Inferable Acceptance Criteria and Reported Device Performance

The document lists categories of performance testing that were conducted with "acceptable results." While specific numerical acceptance criteria and reported performance values are not given, the implication is that the device met internal, pre-defined criteria for these aspects.

A table of acceptance criteria and reported performance cannot be fully constructed from the provided text because specific numerical values for criteria and performance are absent. However, based on the categories mentioned under "Performance Data," we can infer the types of criteria and the general statement of performance:

Study Details (as far as extractable):

1. Sample size used for the test set and the data provenance:

   • The document mentions "bench testing" and a "preclinical animal study." For bench testing, sample sizes are not specified.
   • For the animal study, the model used was a "porcine model," indicating an animal test subject. The number of animals or specific cases within that study is not provided.
   • Data provenance: Porcine model (animal study), laboratory/bench for other tests. Retrospective/prospective is not specified, but animal studies are typically prospective experimental designs.

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

   • The document does not specify the number or qualifications of experts for establishing ground truth, as this device's performance assessment relies on physical and functional tests, and an animal study, rather than expert interpretation of medical images or data.

3. Adjudication method for the test set:

   • Not applicable/Not specified. The performance testing described (bench, animal study) does not involve adjudication processes typically used in clinical imaging or diagnostic studies.

4. 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. This is a medical device, not an AI-assisted diagnostic tool subject to MRMC studies. The clearance is for the physical device itself.

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

   • Not applicable. This is a physical medical device, not an algorithm, so "standalone" performance in the context of AI software is not relevant here.

6. The type of ground truth used:

   • For bench testing: Engineering specifications, physical measurements, functional demonstrations.
   • For the animal study: Observation of safety and performance (e.g., successful tissue approximation, LAMS deployment, absence of adverse events) within the porcine model. This could be considered "outcomes data" in an experimental animal model context.

7. The sample size for the training set:

   • Not applicable. This is a physical medical device. There is no "training set" in the context of machine learning algorithms. Its design would be informed by engineering principles and possibly prior animal/cadaver studies, but not a formal 'training set'.

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

   • Not applicable, as there is no training set mentioned for this type of device.

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