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The M9 UltraClave™ is intended to be used in medical and dental offices, hospitals, clinics, nursing homes, laboratories, and other facilities to sterilize heat and moisture stable reusable items (including dental handpieces) that are compatible with steam sterilization. Refer to Standard Cycle Parameters on the following page for detailed information.

The Midmark M9 UltraClave™ Steam Sterilizer uses saturated steam at high pressures and temperatures to kill infectious bio-organisms. This steam is generated inside the sterilization chamber by an electric heating element. The sterilizer's electronic control system is pre-programmed to complete sterilization cycles according to established time, temperature, and pressure parameters.

Items to be sterilized are placed in the sterilization chamber. The operator chooses a sterilization cycle, and presses the appropriate switch. The sterilizer automatically admits a controlled volume of water to the chamber, heats the water into steam, sterilizes the items, and automatically vents the steam and dries the items after sterilization is complete.

The provided documentation describes the Midmark M9 UltraClave™ Steam Sterilizer. This device is a sterilizer, not an AI/ML powered diagnostic device. Therefore, many of the requested categories related to AI performance, such as sample sizes for test and training sets, expert ground truth establishment, adjudication methods, and MRMC studies, are not applicable.

The "acceptance criteria" for this device are its "Standard Cycle Parameters" which define the time and temperature minimums for various types of sterilization programs. The "study that proves the device meets the acceptance criteria" is implicitly the general validation and testing undertaken by Midmark Corporation to demonstrate its sterilizing capabilities, as inferred by its 510(k) clearance by the FDA. The document states that "the M9 UltraClave™ meets performance standards that the predicate device does not," implying that internal testing was conducted.

However, detailed information on how performance was measured, including specific sample sizes, a detailed test protocol, or explicit statistical results is not present in the provided 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance: Not applicable for this type of device (sterilizer). The "test set" would be various sterilization loads and cycles, but detailed data on the size or provenance of these internal tests is not provided in the 510(k) summary.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a sterilizer is based on the inactivation of microorganisms, which is evaluated through established microbiological testing protocols rather than expert human interpretation.

4. Adjudication method for the test set: Not applicable.

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 not an AI/ML powered device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a standalone device in the sense that it performs sterilization cycles automatically, but it is not an algorithm-only device. Its performance is measured directly (e.g., against biological indicators, temperature/pressure sensors) rather than through comparison to human interpretation.

7. The type of ground truth used: The ground truth for sterilizers is based on the inactivation of resistant microorganisms (e.g., bacterial spores) under defined conditions. This is typically assessed using biological indicators (e.g., Geobacillus stearothermophilus spores) during validation studies, along with chemical indicators and physical monitoring of temperature, pressure, and time.

8. The sample size for the training set: Not applicable. This device does not use a training set in the context of machine learning.

9. How the ground truth for the training set was established: Not applicable.

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