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EG-530N is intended for the visualization of the upper digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the esophagus, stomach, and duodenal bulb. The EG-530N can be inserted orally or transnasally.

FUJIFILM Endoscope Model EG-530N is comprised of three general sections: a control portion, an insertion portion and an umbilicus. The control portion controls the angulation of the endoscope. This portion also controls the flexibility of the distal end in the endoscope. The insertion portion contains glass fiber bundles, several channels and a complementary Charge-Coupled Device (CCD) image sensor in its distal end. The channels in the insertion portion assist in delivering air/suction as well as endoscope accessories, such as forceps. The glass fiber bundles allow light to travel through the endoscope and emit light from the tip of the insertion to illuminate the body cavity. This provides enough light to the CCD image sensor to capture an image and display it on the monitor. The umbilicus consists of electronic components needed to operate the endoscope when plugged in to the video processor and the light source. The endoscope is used in combination with FUJIFILM's video processors, light sources and peripheral devices such as monitor, printer, foot switch, and cart.

This document describes the FUJIFILM Endoscope Model EG-530N. However, the provided text does not contain information about a study proving that the device meets acceptance criteria related to an AI or algorithm's performance. The document is a 510(k) summary for a medical endoscope, focusing on its mechanical and electrical safety, biocompatibility, reprocessing validation, and basic performance characteristics (field of view, bending capability, etc.).

It does not mention:

• Any AI component or algorithm.
• Software performance criteria based on diagnostic accuracy, sensitivity, specificity, or similar metrics typically associated with AI.
• Studies involving human readers with or without AI assistance (MRMC studies).
• Any ground truth establishment by experts for diagnostic purposes.
• Training or test set sample sizes for an algorithm.

The "Performance Data" section refers to engineering and safety tests for the endoscope itself, not an AI or diagnostic algorithm.

Therefore, based on the provided text, I cannot answer the specific questions about acceptance criteria for an AI-driven device, the study proving it, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, training sets, or ground truth establishment relevant to AI.

The acceptance criteria mentioned in the document are for the physical endoscope's performance, such as:

• Field of view
• Bending capability
• Rate of air supply
• Rate of water supply
• Suction rate
• Working length
• Forceps channel diameter
• Viewing direction
• Resolution
• LG output (presumably "Light Guide output" or similar)

The document states: "In all cases, the devices met the pre-defined acceptance criteria for the test." However, it does not provide the specific numerical acceptance criteria or the detailed results for each of these physical parameters.

In summary, your request seems to be looking for information about an AI/algorithm-driven device, but the provided documentation is for a traditional medical endoscope, which does not appear to incorporate AI or machine learning.

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EB-530US is intended for the observation, diagnosis, and endoscopic treatment of the trachea, bronchial tree and surrounding organs using ultrasonic images. It is used with a FUJIFILM ultrasonic processor, video processor, light source, other peripheral equipment and endoscopic accessories. It is not intended for use on children and infants.

FUJIFILM Ultrasonic Endoscope EB-530US is an ultrasonic bronchoscope that emits ultrasound waves and scans the reflected signals to provide ultrasonic images when used in combination with an ultrasonic processor.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

The document does not specify human clinical trials or a "test set" in the context of patient data. The performance testing appears to be entirely non-clinical (engineering and laboratory tests), likely using a sample of the manufactured device itself or components. Therefore, information about human data provenance is not applicable.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The reported performance testing is non-clinical and does not involve expert evaluation for ground truth in a clinical context.

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

Not applicable, as no clinical test set requiring adjudication by experts is described.

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 MRMC comparative effectiveness study was done, and the device is an ultrasonic endoscope, not an AI-assisted diagnostic tool. Therefore, the effect size of human readers improving with AI assistance is not applicable.

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

This is not an AI algorithm. The device is a medical instrument. Therefore, this question is not applicable.

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

For the non-clinical performance tests, the "ground truth" would be established by objective measurements against engineering specifications, calibrated instruments, and established safety and performance standards (e.g., IEC standards for electrical safety, ISO standards for biocompatibility).

8. The sample size for the training set

Not applicable, as this is not an AI/machine learning device requiring a training set.

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

Not applicable, as this is not an AI/machine learning device.

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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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EG-530UT2 and EG-530UR2 are intended to provide ultrasonic images of submucosal and peripheral organs of the upper gastrointestinal tract for observation, diagnosis, and endoscopic treatment. The product is intended to be used with a Fujifilm ultrasonic processor. This product is not intended for use on children and infants.

FUJIFILM Ultrasonic Endoscopes EG-530UT2 and EG-530UR2 are upper gastrointestinal endoscopes that emit ultrasound waves and scan the reflected signals to provide ultrasonic images when used in combination with an ultrasonic processor.

The provided text describes a 510(k) premarket notification for FUJIFILM Ultrasonic Endoscopes EG-530UT2 and EG-530UR2. This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving that a new AI/software device meets specific performance criteria through a clinical study.

Therefore, the requested information regarding acceptance criteria for an AI/software device and a study to prove it meets them cannot be fully extracted from this document. The document lists performance tests for the physical endoscopic devices (e.g., field of view, bending capability, resolution, air/water supply rates), but these are for hardware performance, not AI/software performance.

However, I can extract the information related to the device performance and acceptance criteria for the physical endoscopy device as described in the document.

Here's a breakdown of what can be extracted and what cannot be, based on the provided text:

What Can Be Extracted (for the physical endoscope device):

1. A table of acceptance criteria and the reported device performance:
   The document states: "Fujifilm conducted the following performance testing on the proposed devices EG-530UT2 and EG-530UR2 to ensure that the modified devices perform equivalently to the predicate devices:

   • Field of view
   • Viewing direction
   • Bending capability
   • Resolution
   • Air supply rate
   • Water supply rate
   • Axial resolution
   • LG output
   • Suction rate
   • Working length
   • Lateral resolution
   • Penetration depth
   • Forceps channel diameter

   In all cases, the devices met the pre-defined acceptance criteria for the test."

   Therefore, the table would look like this (specific values are not provided in the document, only the claim of meeting criteria):

   Acceptance Criteria (Measured Parameter)	Reported Device Performance
   Field of view	Met pre-defined criteria
   Viewing direction	Met pre-defined criteria
   Bending capability	Met pre-defined criteria
   Resolution	Met pre-defined criteria
   Air supply rate	Met pre-defined criteria
   Water supply rate	Met pre-defined criteria
   Axial resolution	Met pre-defined criteria
   LG output	Met pre-defined criteria
   Suction rate	Met pre-defined criteria
   Working length	Met pre-defined criteria
   Lateral resolution	Met pre-defined criteria
   Penetration depth	Met pre-defined criteria
   Forceps channel diameter	Met pre-defined criteria

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

   • Sample Size: Not explicitly stated for each performance test. The document refers to "the proposed devices EG-530UT2 and EG-530UR2" performing these tests, suggesting testing on the physical devices themselves rather than a separate dataset of patient cases.
   • Data Provenance: Not specified. This is a pre-market submission, and the tests are likely internal lab/bench testing of the device hardware.

What Cannot Be Extracted (as it's not an AI/software performance study):

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for physical device performance typically comes from engineering specifications and measurements, not expert human interpretation of medical images.
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 does not describe an AI-assisted diagnostic device or an MRMC study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, as this is not an AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for an AI/software context. Ground truth for physical device specs (like resolution, field of view) is based on engineering measurements against design specifications.
8. The sample size for the training set: Not applicable. This document is not about AI training.
9. How the ground truth for the training set was established: Not applicable.

In summary: The provided FDA 510(k) clearance document for FUJIFILM Ultrasonic Endoscopes EG-530UT2 and EG-530UR2 pertains to demonstrating substantial equivalence of a physical medical device (endoscope) to a predicate, primarily through engineering performance testing (e.g., field of view, bending capability, resolution, fluid rates), electrical safety, and biocompatibility. It is not a document describing the validation of an AI/software device, and therefore the specific criteria related to AI performance studies cannot be found within it.

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