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This device is intended for the visualization of the lower digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the rectum and large intestine.

The modified Fujifilm Endoscope Models EC-600HL and EC-600LS are comprised of three main sections: an operation section, an insertion portion, and an umbilicus. The operation section controls the angulation (up/down/left/right) of the distal end of the endoscope. The insertion contains class fiber bundles, several channels and a complementary metal-oxide-semiconductor (CMOS) image sensor. The glass fiber bundles allow light to travel through the endoscope and emit light from the tip of the insertion portion to illuminate the body cavity. This provides enough light to the CMOS image sensor to capture an image and display it on the monitor. The endoscope also contains several channels to deliver air/water and provide suction, as well as a forceps channel. The forceps channel is used to introduce endoscope accessories such as biopsy forceps during the procedure. The umbilicus section consists of electronic components needed to operate the endoscope when plugged in to the video processor and the light source.

The provided text is a 510(k) summary for the Fujifilm Endoscope Models EC-600HL and EC-600LS. This document describes modifications to existing devices and asserts substantial equivalence to predicate devices, rather than presenting a study for a novel device to meet specific acceptance criteria for a new clinical indication. Therefore, much of the requested information (e.g., sample size for test/training sets, data provenance, number of experts for ground truth, MRMC study results, standalone performance) is not applicable or not provided in this regulatory submission for device modifications.

Here's the available information based on your request:

Acceptance Criteria and Reported Device Performance

The submission states that performance testing was conducted for the modified devices to "ensure that the modified device performs equivalently to the predicate EC-600HL and EC-600LS." The acceptance criterion for each test was that "the device met the pre-defined acceptance criteria for the test," implying equivalence to the predicate device's performance specifications.

(Note: The document only states that the device 'met pre-defined acceptance criteria' for each test. It does not provide the specific numerical criteria or the measured performance values. The acceptance criteria are implicitly based on the performance characteristics of the predicate device.)

Other Requested Information:

1. Sample size used for the test set and the data provenance: Not explicitly stated. The performance data seems to be based on engineering and bench testing, not clinical studies with human subjects or retrospective data.
2. 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 for this type of engineering performance testing. Ground truth for these tests would likely be established through calibrated measurement equipment and established engineering standards.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable for this type of engineering performance testing.
4. 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 submission is for modifications to an endoscope, not an AI-powered diagnostic device.
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an algorithm-only device.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the performance characteristics listed, the ground truth would typically be established through physical measurements, calibration, and engineering specifications, rather than clinical outcomes or expert consensus.
7. The sample size for the training set: Not applicable. This is not a machine learning or AI device that requires a training set in the conventional sense.
8. How the ground truth for the training set was established: Not applicable.

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