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This product is intended to supply CO2 gas and feed water to clean lenses in the gastrointestinal tract when used as an accessory with Fujifilm's endoscopy system.

The Fujifilm Endoscopic CO2 Regulator GW-100 supplies CO2 gas to insufflate the gastrointestinal tract and water to wash the endoscope lens during an examination. The GW-100 is similar to Olympus' XECR-2 Endoscopic Insufflation Unit. The scientific fundamental technology and operating principle of the subject and predicate devices are similar. Both devices utilize a solenoid/decompression valve mechanism to dispense CO2. CO2 can either be supplied from a CO2 gas cylinder or from a facility's main CO2 supply.

The GW-100 can be used with any Fujnon/Fujifilm gastrointestinal endoscope, Fujinon/Fuiifilm Video Processor/Light Source system, video monitor, footswitch, cart, endoscopic accessories, electrosurgical unit and other peripheral devices used for endoscopy.

The provided document is a 510(k) summary for the Fujifilm Endoscopic CO2 Regulator GW-100. It details the device's intended use and substantial equivalence to a predicate device, but it does not contain information about acceptance criteria for device performance or a study demonstrating that the device meets those criteria, as typically found for AI/ML-based devices.

This document describes a medical device seeking clearance based on substantial equivalence, which is a different regulatory pathway than proving performance against specific clinical acceptance criteria for new AI/ML-based diagnostic or therapeutic devices. The studies mentioned are primarily related to electrical safety, electromagnetic compatibility (EMC), software validation, and biocompatibility, not clinical performance metrics like sensitivity, specificity, or reader improvement.

Therefore, most of the requested information cannot be extracted from this document.

Here's what can be gathered, with notes explaining the absence of other details:

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

• Acceptance Criteria: Not explicitly stated in terms of clinical performance metrics. The device's "acceptance" is based on its substantial equivalence to a predicate device and meeting safety/performance standards (electrical safety, EMC, software, biocompatibility).
• Reported Device Performance: Performance is demonstrated through compliance with safety and engineering standards. No clinical performance metrics (e.g., accuracy, sensitivity, specificity) are reported.

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

• Not applicable for this type of submission. This submission relies on substantial equivalence and non-clinical engineering tests, not a clinical test set with patient data for performance evaluation.

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)

• Not applicable. No clinical ground truth was established for a test set in this submission.

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

• Not applicable. No clinical test set or adjudication was performed.

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

• No. This is a hardware device (CO2 regulator), not an AI-assisted diagnostic or therapeutic tool. Therefore, an MRMC study is not relevant.

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

• Not applicable. This device is a hardware accessory for an endoscopy system, not a standalone algorithm.

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

• Not applicable. No clinical performance ground truth was established. The "ground truth" here is compliance with established engineering and safety standards.

8. The sample size for the training set

• Not applicable. As this is not an AI/ML device, there is no concept of a training set for an algorithm. Device validation was based on testing against design specifications and international standards.

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

• Not applicable. See point 8.

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