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The device is intended for the visualization of the upper digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the esophagus, stomach, and duodenum.

FUJIFILM 600 Series Endoscope EG-600WR v2 is an upper gastrointestinal endoscope that captures 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 endoscope, 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.

The provided text describes the Fujifilm 600 Series Endoscope EG-600WR v2, a medical device intended for the visualization and treatment of the upper digestive tract. It details a 510(k) premarket notification for this device, claiming substantial equivalence to a predicate device (Fujifilm 600 Series Endoscopes EC-600WL and EG-600WR, K132210).

However, the document does not contain information about an AI/ML-driven medical device, nor does it describe a study involving algorithms, human-in-the-loop performance, or reader studies with experts for ground truth establishment.

The provided text focuses on the endoscope device itself, and the modifications made to it. The performance testing outlined is for the physical device's characteristics (e.g., field of view, bending capability, resolution, safety, and reprocessing).

Therefore, I cannot provide the requested information regarding acceptance criteria and studies for an AI/ML device because the document describes a physical medical endoscope and not an AI/ML system.

If the request refers to the acceptance criteria for the physical endoscope, the available information is as follows:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states: "In all cases, the device met the pre-defined acceptance criteria for the test." However, the specific numerical acceptance criteria for each performance parameter are not explicitly listed in the provided text. Only the parameters tested are mentioned.

2. Sample sized used for the test set and the data provenance: Not applicable as this is a physical device and not a data-driven AI/ML system. The testing involved samples of the physical endoscope.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth as typically understood for AI/ML models (e.g., expert consensus on image findings) is not relevant for the performance testing of a physical endoscope.

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. This document is not about an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): For the physical device, performance was evaluated against technical specifications and consensus standards (e.g., ISO 8600-1 for endoscopic performance, IEC and ISO for safety and biocompatibility).

8. The sample size for the training set: Not applicable. This is a physical device, not an AI/ML model.

9. How the ground truth for the training set was established: Not applicable. This is a physical device, not an AI/ML model.

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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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