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The enspire™ 3000 Series Cleaning and Liquid Chemical Sterilant Processing System is intended to effectively provide a pressure monitor, clean, provide liquid chemical sterilization, rinse, and air purge validated immersible, reusable, semi-critical, heat sensitive medical devices such as flexible endoscopes and their accessories.

The validated cleaning process replaces cleaning for endoscopes other than duodenoscopes. Manual cleaning of duodenoscopes according to the manufacturer's written instructions for use is required prior to placement in the enspire™ 3000 Series Processor.

The enspire™ 3000 Series Processor uses only Revital-Ox 2X Concentrate Enzymatic detergent to clean and S40 Sterilant Concentrate to liquid chemically sterilize medical devices. It automatically dilutes the S40 Sterilant Concentrate to its use dilution (> 1820 mg/L peracetic acid), liquid chemically sterilizes the load during a controlled 6-minute exposure at 46.6 to 55°C and rinses the load with 0.2-micron filtered water.

The enspire 3000 Cleaning and Liquid Chemical Sterilant Processing System (CLCSPS) is a medical device processing system used for cleaning and liquid chemically sterilizing immersible, reusable, semi-critical, heat-sensitive devices such as flexible endoscopes and their accessories. The system consists of the enspire 3000 Processor, Revital-Ox 2X Concentrate Enzymatic Detergent, S40 Sterilant Concentrate and Max Flow Connectors.

The enspire 3000 Cleaning and LCSPS is an automated, self-contained device for the effective cleaning and liquid chemical sterilization of semi-critical medical devices and their accessories. The devices will not require manual cleaning prior to processing in the enspire 3000 Processor with the exception of duodenoscopes which must be manually cleaned per the manufacturer's instructions for use. In addition, prior to placement in the enspire 3000 processor, the device will undergo a manual leak test and the user will ensure the lumens are not blocked. If the device has internal channels or lumens, Max Flow Connectors are used to facilitate the delivery of detergent, sterilant use-solution and rinse water to internal channels.

Once the device is positioned in the enspire 3000 processor, the unit will create and maintain the conditions necessary for effective cleaning and liquid chemical sterilization of the load. At the beginning of the processing cycle, automated pressure monitoring is performed to confirm the integrity of the flexible endoscope throughout the process. The enspire 3000 processor maintains inflation of the processed device to prevent ingress of fluid during processing and the pressure monitoring is repeated at the end of the processing cycle to ensure that the device is leak tight after cleaning and liquid chemical sterilization. At the end of the processing cycle, the cleaned and liquid chemically sterilized devices are rinsed with 0.2 micron filtered potable water followed by a HEPA-filtered air purge to aid in drying the endoscope. The processor, which is computer controlled and continually monitored, provides documentation of each cycle.

The enspire 3000 processor utilizes Revital-Ox 2X Concentrate Enzymatic Detergent for cleaning and S40 Sterilant Concentrate for liquid chemical sterilization. S40 Sterilant Concentrate is a single use chemical sterilant concentrate; its active ingredient, peracetic acid, is combined with inert ingredients (builders) to form a use dilution which inhibits corrosion of metals, polymers and other materials.

The provided text is an FDA 510(k) summary for a medical device called the "enspire 3000 Cleaning and Liquid Chemical Sterilant Processing System." This document primarily focuses on demonstrating substantial equivalence to a predicate device, specifically for a modification to the existing device (changing materials in the Chemical Delivery System).

As such, the document does not describe a study involving human readers, AI assistance, ground truth labels from experts for diagnostic tasks, or a training set for an AI model. The "device" in question is not an AI/ML algorithm for image analysis or diagnosis. Instead, it's a machine for sterilizing endoscopes.

Therefore, many of the requested points, such as sample size for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone AI performance, and AI model training details, are not applicable to this type of device and the information provided in the document.

However, I can extract the acceptance criteria and performance related to the modification of this physical device.

Here's the relevant information based on the provided text:

Acceptance Criteria and Device Performance for the Modified Enspire 3000 System

The study described is not for an AI/ML diagnostic device, but rather for a physical medical device (an endoscope reprocessor) that has undergone a material change in one of its components. The acceptance criteria and performance relate to the safety and efficacy of the modified reprocessing system.

1. Table of Acceptance Criteria and Reported Device Performance

Regarding the other requested information, which is primarily relevant for AI/ML diagnostic devices, it is not found in this document because the device is not an AI/ML system.

Here's why each point is Not Applicable (N/A) in this context:

• 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

  • N/A: The "test set" here refers to physical tests conducted on the modified device and its components, not a dataset of medical images or patient data. The document mentions "multiple processing cycles" and "representative endoscope" but no specific numeric sample sizes for these qualitative/quantitative tests. Data provenance in terms of country of origin or retrospective/prospective is not relevant for this type of device testing.

• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

  • N/A: Ground truth for diagnostic AI models is established by human experts (e.g., radiologists). For this device, "ground truth" is based on established engineering and chemical standards (e.g., ISO standards, acceptable chemical residual levels, non-cytotoxicity). No human experts are described as "establishing ground truth" in the way they would for a diagnostic AI study.

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

  • N/A: Adjudication methods are used in AI studies where multiple human readers disagree on a diagnosis or annotation. This is a physical device subject to engineering and chemical validation 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

  • N/A: An MRMC study is relevant for evaluating the impact of AI assistance on human diagnostic performance. This device is an endoscope reprocessor, having no direct interaction with human diagnostic reading.

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

  • N/A: "Standalone performance" refers to the accuracy of an AI algorithm in performing a diagnostic task without human intervention. This device performs a physical reprocessing function, not a diagnostic one.

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

  • N/A: As explained in point 3, the "ground truth" for this device's performance relies on engineering specifications, chemical analysis, and biocompatibility standards rather than medical diagnostic ground truth sources like pathology or patient outcomes.

• 8. The sample size for the training set

  • N/A: This device is a physical machine, not an AI/ML model that requires a "training set."

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

  • N/A: As this is not an AI/ML model, there is no training set or associated ground truth to establish in that context.

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