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The InScope Multi-Clip Applier is indicated for use with an endoscope to place clips in the gastrointestinal (GI) tract for the purpose of:

• Endoscopic marking, .
• Hemostasis for: ●
  • o Mucosal and submucosal defects

The InScope™ Mult-Clip Applier is a sterile, single patient use, disposable instrument capable of attaching clips to the mucosal lining of the gastrointestinal (GI) tract. The clips are used to create hemostasis and/or tissue approximation. The distal, flexible portion of the applier is designed to work with a flexible endoscope having a minimum working channel diameter of 3.2 mm. Two opposing jaws grasp tissue via a manually activated lever on a proximal handle. Once the closure lever is latched, a firing mechanism is manually activated, which deploys a titanium clip over the target tissue. The device is supplied preloaded with four (4) titanium clips.

The provided text describes the 510(k) summary for the InScope™ Multi-Clip Applier. This is a medical device submission, and the "study" referred to is the pre-clinical and bench testing conducted to demonstrate substantial equivalence to predicate devices, rather than a clinical trial involving human subjects or AI algorithms. As such, many of the typical questions for AI/algorithm performance studies do not apply.

Here's an analysis based on the provided text:

Acceptance Criteria and Device Performance

The "acceptance criteria" for this device are demonstrating substantial equivalence to predicate devices in specific performance aspects. The reported device performance is that it meets these criteria.

Table of Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • The document refers to "bench and preclinical testing" and "preclinical data." It does not specify sample sizes for these tests.
   • Data provenance is preclinical/bench testing. No information on country of origin is provided, but typically, these tests are conducted by the manufacturer (Ethicon Endo-Surgery, Inc. in Cincinnati, OH, USA). The data is retrospective in the sense that it refers to testing done before the 510(k) submission.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:

   • This question is not applicable. The "ground truth" for a medical device like this is based on measurable physical properties and biological responses in preclinical models (e.g., tissue viability, clip retention force, hemostasis effectiveness), not on expert interpretations of medical images or patient outcomes in a clinical setting in the way an AI algorithm would be evaluated. These assessments are typically performed by engineers, biologists, or veterinarians specializing in device testing. The document does not specify the number or qualifications of these individuals.

3. Adjudication Method for the Test Set:

   • This question is not applicable. Adjudication methods like 2+1 or 3+1 are used for human reader studies or to resolve discrepancies in expert labeling, which is not relevant here.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI algorithms that assist human readers in interpreting medical data (e.g., radiology images). The InScope Multi-Clip Applier is a physical surgical tool; its effectiveness is evaluated through direct performance metrics, not by how it influences human interpretation.

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

   • Not applicable. The InScope Multi-Clip Applier is a physical device used by a clinician, not an algorithm. Its performance is inherently "with human-in-the-loop" as it requires manual operation.

6. The Type of Ground Truth Used:

   • For the preclinical testing, the "ground truth" was based on direct measurements and observations of physical performance and biological responses in laboratory and animal models. This includes:
     • Measured hemostatic effectiveness.
     • Measured clip retention force/durability.
     • Histopathological assessment of tissue viability after clip placement.
     • Observation of outcomes for procedures like perforation closure in preclinical models.

7. The Sample Size for the Training Set:

   • Not applicable. This device does not use an AI algorithm that requires a training set. The "design and development" process would involve iterative testing and refinement, but this is distinct from machine learning training.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable. As there is no AI algorithm or training set, this question does not apply.

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