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The Instrument Case is intended for staff involved in reprocessing of reusable instruments, and for surgical teams.
The product shall only be used:

• · in a controlled surgical environment under sterile conditions such as a hospital,
• · in reprocessing environment at sterilization departments or reprocessing centers,
• · and for transport of surgical instruments.
  Sterilization parameters:
  In US: Pre-Vacuum Steam at 132 ℃ (270 °F) for 4 min with a 20 minutes dry time Outside US: See the Reprocessing Guide available in your country
  The worse-case validated load for the Instrument Case, including instruments, is 1700 g.

The Instrument case, Figure 1, is a reusable sterilization container intended for use in health care facilities for the purpose of containing reusable medical devices for sterilization. The specific use for the Instrument case is to hold reusable instruments during transport, the sterilization process, and during surgery.
The Instrument case consists of tray and lid made of stainless steel with a small box included, which is a component tray. The grommets, strips and holders that keep the instruments in place are made of silicone or stainless steel, and the latches in the lid are made of a Thermoplastic resin, Santoprene. The packaging materials are made of polyethylene and polyolefin.

Based on the provided text, the device in question is an "Instrument Case" (K231604) and the performance data presented is for its cleaning, sterilization, and lifecycle durability, not for a complex AI/ML diagnostic or image analysis system. Therefore, many of the typical acceptance criteria and study components for AI-driven medical devices (like MRMC studies, ground truth based on expert consensus/pathology, training set details, etc.) are not applicable here.

This document describes a "Special 510(k)" submission for an updated Instrument Case, where the key is demonstrating substantial equivalence to a predicate device (K223672) through non-clinical data. The updates are minor (additional etchings and validation for a few more instruments).

Acceptance Criteria and Reported Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document provides a clear table (Table 2) outlining the tests, acceptance criteria, and results for the Instrument Case.

Study Details (as applicable to this type of device)

Since this is for a physical medical device (Instrument Case) and not an AI/ML diagnostic, the following points address what's relevant from the provided text and note where typical AI/ML study details are not applicable:

2. Sample sizes used for the test set and the data provenance:

   • Sample Size: The document does not specify exact numbers of Instrument Cases tested for each specific test, but implies multiple units ("All units" met criteria for cleaning, "devices were inoculated" for sterilization, "Validation of 25 cycles of reprocessing" for lifecycle testing). Given the nature of a component, this is likely a small sample (e.g., 3-5 devices) for each test type, subjected to repetitive cycles (e.g., 25 cycles for lifecycle).
   • Data Provenance: Not explicitly stated, but these are laboratory bench tests conducted to regulatory standards (AAMI, ISO). The manufacturer is Cochlear Bone Anchored Solutions AB in Sweden, but the testing would be conducted in certified testing labs, not tied to a specific clinical population or country in the way clinical data for AI/ML devices would be. These are non-clinical, controlled bench tests. The tests themselves are prospective as they are specifically conducted for this submission.

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

   • Not Applicable. For a physical device like an instrument case, "ground truth" is established by direct measurement and observation against objective criteria (e.g., visual inspection for soil, chemical analysis for protein/hemoglobin, bacterial growth assessment, physical damage assessment). No human expert interpretation (like a radiologist reading an image) is involved beyond the technician performing the tests and recording the results.

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

   • Not Applicable. As described above, the acceptance criteria are objective and quantitative (e.g., µg/cm², SAL target, visual absence of damage). There is no multi-reader or adjudication process for interpreting "ground truth" for these physical performance tests.

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 is a physical medical device (sterilization case), not an AI/ML diagnostic or image analysis tool that would assist human readers.

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

   • Not Applicable. This is a physical medical device. There is no algorithm. The "standalone" performance here refers to the device's ability to meet its performance specifications (cleaning, sterilization, durability) without human intervention beyond setting up and running the test parameters.

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

   • The "ground truth" for the Instrument Case's performance is based on objective, quantitative measurements and direct visual inspection following standardized protocols and accepted industry standards (AAMI, ISO). Examples include:
     • Chemical assays for residual protein and hemoglobin.
     • Microbiological testing for Sterility Assurance Level (SAL).
     • Physical inspection for moisture, corrosion, and damage.
     • Toxicological assessment based on chemical characterization.

8. The sample size for the training set:

   • Not Applicable. There is no "training set" in the context of a physical device like this. Training sets are specific to AI/ML model development.

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

   • Not Applicable. There is no training set.

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