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This product is a single use distal end cap required to be attached to certain FUJIFILM endoscopes during clinical use to assist in providing atraumatic passage of the endoscope tip during endoscopic procedures.

Do not use this product for any other purposes.

FUJIFILM Distal End Cap DC-08D is a single use device that is supplied sterile, eliminating the need for reprocessing prior to use. This distal end cap is designed to the distal tip of a FUJIFILM ED-580XT duodenoscope. After an endoscopic procedure, the DC-08D is detached to facilitate easier access to the distal elevator during reprocessing of the ED-580XT.

The available document is a 510(k) Summary for the FUJIFILM Distal End Cap DC-08D. It details the device's characteristics and compares it to a predicate device (FUJIFILM Distal End Cap DC-07D). However, it does not contain details about a clinical study involving human readers or an AI algorithm’s standalone performance, as the device is a medical accessory, not an AI diagnostic tool.

Therefore, many of the requested sections about acceptance criteria and study details for AI performance are not applicable and cannot be extracted from this document.

Here's the information that can be extracted, along with an explanation for the unavailable sections:

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

The document mentions that "Additional performance specifications were evaluated against pre-determined acceptance criteria" and that "Bench testing was conducted to demonstrate that the subject device is attached securely such that it will not detach during use." However, it does not explicitly list the specific acceptance criteria or quantitative performance results in a table format. It only states that the device was evaluated for conformance to ISO 8600-1:2015 and other standards.

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

This information is not applicable as the document describes the regulatory submission for a physical medical device (an end cap), not an AI algorithm requiring a test set of data. The "testing" mentioned refers to bench testing, sterility testing, and biocompatibility testing of the physical product, not data analysis.

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)

This information is not applicable. Ground truth for data analysis is not relevant for this physical device submission. The "ground truth" for this device would be established by validated engineering and scientific methods and standards (e.g., successful sterilization, material safety, secure attachment), not by expert consensus on data interpretation.

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

This information is not applicable for the reasons stated above.

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

This information is not applicable. The device is an end cap for an endoscope, not an AI system designed to assist human readers.

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

This information is not applicable. The device is a physical medical accessory, not an algorithm.

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

As mentioned, the concept of "ground truth" for data analysis is not applicable here. The verification of the device's properties relies on scientific and engineering testing methods, such as:

• Sterility verification: Based on biological indicators and established sterilization cycles.
• Biocompatibility verification: Based on in-vitro and/or in-vivo tests according to ISO standards.
• Mechanical integrity: Based on bench testing to ensure secure attachment and durability during use.

8. The sample size for the training set

This information is not applicable as there is no AI algorithm and therefore no training set for data.

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

This information is not applicable as there is no AI algorithm and therefore no training set.

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