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The RAS Racks are used in the RAS Cycle of the AMSCO 7052HP Single-Chamber Washer/Disinfector and the AMSCO 7053HP Single-Chamber Washer/Disinfector for the effective cleaning, rinsing, intermediate level disinfection and drying of reusable da Vinci® X/Xi and S/Si EndoWrist® instruments.

The RAS Racks are designed to enable the mechanical cleaning, rinsing, and disinfection of up to twelve (12) robotic-assisted surgery instruments in a compatible washer-disinfector. Twelve soiled da Vinci X/Xi and S/Si Endowrist® instruments, with limited prior manual pre-cleaning, are loaded into the appropriate RAS Rack according to the provided instructions for use. The rack is placed in the AMSCO 7052HP or 7053HP Single-Chamber Washer/Disinfector, and the RAS Cycle is selected.

The RAS Cycle performs automated cleaning through a validated series of spraywashing, lumen flushing steps that use Prolystica Ultra Concentrate HP Enzymatic Cleaner alternately with Prolystica Ultra Concentrate HP Neutral Detergent in temperature-controlled solutions. When the series of automated prewash and washing stages are complete, a one-minute rinse occurs. Next the RAS Cycle completes a thermal rinse to achieve intermediate level disinfection of the load before drying it. Upon RAS Cycle completion, the devices are ready to be prepared and packed for steam sterilization.

The provided text is related to a 510(k) premarket notification for STERIS RAS 12 Rack / RAS 12 Long Rack, used in a washer-disinfector for surgical instruments. This is a medical device, specifically an accessory for automated cleaning and disinfection.

The request asks for details about "acceptance criteria and the study that proves the device meets the acceptance criteria," with a focus on an AI/Machine Learning context. However, the provided document does not describe an AI/Machine Learning device or any study involving AI performance.

Instead, it details the substantial equivalence of a physical medical device (racks for a washer-disinfector) to a legally marketed predicate device. The "performance testing" mentioned is non-clinical and relates to the cleaning and disinfection efficacy of the washer-disinfector system for surgical instruments, not an AI model's diagnostic accuracy or similar metrics.

Therefore, most of the requested information, which is specific to AI/ML device studies (such as sample size for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, and effect size for human reader improvement with AI), is not present in this document.

The document states:

• "The purpose of this Special 510(k) is to update labeling consistent with a labeling revision being enacted by Intuitive Surgical to allow for automated cleaning and intermediate level disinfection of reusable da Vinci X/Xi and S/Si EndoWrist® instruments in the RAS Racks and RAS Cycles of the AMSCO 7052HP and 7053HP Single-Chamber Washer/Disinfectors. The RAS Racks and associated RAS Cycles were not altered in anyway."
• "Based on the intended use, technological characteristics and non-clinical performance data, the subject device is as safe, as effective and performs as well or better than the legally marketed predicate device (K200577), Class II (21 CFR 876.1500), product code NVE."

This indicates that the "study" is a non-clinical performance test to establish cleaning and disinfection efficacy, likely involving standards for residual protein, blood, or other contaminants, rather than diagnostic accuracy.

Given the provided text, I cannot answer the questions specific to AI/ML performance studies because the document does not pertain to an AI/ML device.

I can, however, extract information about the device characteristics and the basis for its clearance as presented in the document, which primarily relies on substantial equivalence to a predicate device and non-clinical performance data for cleaning/disinfection.

What I can infer from the document regarding the device and its assessment:

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

• The document doesn't present "acceptance criteria" in a typical table format with quantitative performance metrics for a diagnostic device. Instead, it relies on demonstrating that the proposed device has "identical" or "similar" technological characteristics and performs "as well or better" than its predicate device through non-clinical performance testing related to cleaning, rinsing, disinfection, and drying efficacy.
• The "performance" referred to is the successful demonstration of effective cleaning and intermediate level disinfection of specific surgical instruments (da Vinci® X/Xi and S/Si EndoWrist® instruments) when processed in the RAS Racks within the specified washer-disinfector cycles. This would involve meeting established benchmarks for sterility or cleanliness, though the specific criteria (e.g., maximum allowable protein residue) are not detailed in this summary. The table provided is a "Technological Characteristics Comparison Table" comparing the proposed device to the predicate, not a performance table against specific acceptance criteria.

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

• Not applicable/mentioned for an AI/ML context. Likely involves a set number of contaminated instruments to be processed and tested, but specific "sample sizes" (e.g., number of instruments, number of cycles) are not detailed in this summary.
• Data provenance (country of origin, retrospective/prospective) is not relevant to this type of device assessment and is not mentioned.

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

• Not applicable. "Ground truth" in this context would be laboratory testing results for cleanliness/disinfection, not expert clinical interpretation.

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

• Not applicable. This is for AI/ML diagnostic 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 does not describe an AI-assisted diagnostic device.

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

• Not applicable. This is a physical device used for cleaning/disinfection.

7. The type of ground truth used:

• For the non-clinical performance testing referenced, the "ground truth" would be established via laboratory assays measuring the reduction of biological contaminants (e.g., protein, hemoglobin) or microbial load on the instruments after processing. This is a direct measurement of the device's efficacy in cleaning and disinfection, not an expert consensus or outcomes data in the clinical sense.

8. The sample size for the training set:

• Not applicable. This is for AI/ML devices.

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

• Not applicable. This is for AI/ML devices.

In summary, the provided document does not contain information relevant to an AI/ML device study or its performance criteria. It describes a 510(k) clearance for a non-AI medical device based on substantial equivalence and non-clinical performance testing for cleaning and disinfection efficacy.

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