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These devices are intended for the lower digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the rectum and large intestine.

FUJIFILM Endoscope Models EC-600HL and EC-600LS are lower gastrointestinal endoscopes that capture images when used in combination with a video processor and light source. Light travels from the light source, through the glass fiber bundles in the endoscopes, and out 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.

This document describes the 510(k) premarket notification for the FUJIFILM Endoscope Models EC-600HL and EC-600LS. The submission aims to demonstrate substantial equivalence to a previously cleared predicate device (K162622). As such, the study performed is a performance testing of the device itself rather than a study on an AI algorithm. Therefore, many of the typical questions for AI/ML study design (e.g., sample size for test/training sets, data provenance, ground truth establishment, MRMC studies) are not applicable in this context.

Here's an analysis of the provided information concerning the device's performance and acceptance criteria:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that "In all cases, the devices met the pre-defined acceptance criteria for the test." However, the exact quantitative acceptance criteria for each test are not explicitly provided in the submitted text. The performance data section lists the parameters tested, but not the specific thresholds for acceptance.

2. Sample Size Used for the Test Set and Data Provenance

This is a physical device performance test rather than an AI/ML algorithm evaluation. The document does not specify the sample size of devices used for testing. It also does not specify data provenance as it's not a data-driven AI study.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not applicable. The performance testing is for physical device characteristics, not for diagnostic accuracy requiring expert interpretation.

4. Adjudication Method for the Test Set

This information is not applicable as this is not an AI/ML study requiring expert adjudication of results.

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 performance evaluation of a physical endoscope, not an AI-assisted diagnostic tool. Therefore, an MRMC study is not relevant.

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

No. This approval is for an endoscope as a physical medical device, not an algorithm.

7. The type of ground truth used

The "ground truth" here would be the physical and functional specifications of the endoscope. For example, for "Field of view," the ground truth would be the expected angular range, and the device's measurement would need to fall within the accepted tolerance of that specification. The document implies these are established engineering specifications rather than clinical ground truth (e.g., pathology).

8. The sample size for the training set

Not applicable. There is no AI model or training set involved in this device approval.

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

Not applicable. There is no AI model or training set involved. The ground truth for this device's performance would be engineering and design specifications.

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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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The FUJIFILM Endoscope Models EC-600HL and EC-600LS are intended for the visualization of the lower digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the rectum and large intestine.

The 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 controls the angulation (up/down/left/right) of the distal end of the endoscope. The insertion contains glass fiber bundles, several channels and a complementary metal-oxide-semiconductor (CMOS) image sensor. The qlass 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 subject device is used in combination with FUJIFILM's video processor, light source and peripheral devices such as water tank, endoscope accessories, monitor, printer, electrosurgical instruments, foot switch, and cart. All of these were previously cleared in K132210.

This document is a 510(k) summary for the Fujifilm Endoscope Models EC-600HL and EC-600LS. It primarily focuses on demonstrating substantial equivalence to a predicate device (FUJIFILM 600 Series Endoscope EC-600WL) rather than establishing novel acceptance criteria or a comprehensive study proving a device meets specific performance metrics for image interpretation by an AI.

Therefore, many of the requested categories for AI-related performance studies cannot be filled as this document concerns a physical endoscope device and its safety/effectiveness, not an AI algorithm for image analysis.

Here's an attempt to address the requested information based only on the provided text, recognizing that much of it is not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not specify discrete acceptance criteria in terms of numerical performance metrics for clinical outcomes (e.g., diagnostic accuracy, sensitivity, specificity) for the endoscope's visualization capabilities. Instead, the "acceptance criteria" are implied by adherence to recognized consensus standards for safety and performance of medical devices. The reported device performance is that it meets these standards and is substantially equivalent to the predicate.

Regarding AI-specific questions:

Since this document describes a physical endoscope, not an AI-powered diagnostic device or software, the following information is not present in the provided text:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. The "performance data" refers to technical bench testing against consensus standards, not a clinical study with patient data used for AI 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. Ground truth for clinical diagnostic performance is not established as this is a hardware submission, not an AI diagnostic tool.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable in the context of AI diagnostic performance. The "ground truth" for the endoscope itself is its demonstrable adherence to safety and performance standards through engineering and biocompatibility testing.
8. The sample size for the training set: Not applicable. There is no AI training set mentioned.
9. How the ground truth for the training set was established: Not applicable. There is no AI training set mentioned.

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