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The Designs for Vision Fiberoptic Light is indicated for use in surgery and medical applications where high intensity illumination is required.

The Designs for Vision Fiberoptic Light is composed of a high intensity light source, fiberoptic cables, and fiberoptic headsets. The Designs for Vision headsets have been marketed since the early 1970s with a long history of safe use in the surgical suite. The headsets are coaxial, bifurcated, or focusable designs.

The light source includes a chuck for fiberoptic cable attachment. The Light source provides a 150-watt power output and contains a continuous illumination level adjustment, which provides 3200 K color temperature light.

The provided text is a 510(k) summary for the Designs for Vision Fiberoptic Light. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, it does not contain the detailed information required to describe acceptance criteria and a study proving those criteria are met in the format requested.

Specifically, the document states: "Testing has been performed which demonstrates the electrical safety and electromagnetic compatibility characteristics of the Designs for Vision Fiberoptic Light." but it does not provide any specifics about this testing, performance outcomes, or acceptance criteria.

Therefore, I cannot populate the requested table and answer the study-specific questions based on the provided text.

Here is what I can extract based on the available information, noting the significant gaps:

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

Missing Information for Table:

• Specific quantitative or qualitative acceptance criteria for electrical safety and EMC.
• Detailed performance results for each criterion.

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

• Sample Size (Test Set): Not specified.
• Data Provenance: Not specified.

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 as the document does not describe a study involving expert-established ground truth for a test set. The validation seems to be primarily through regulatory compliance and substantial equivalence to existing devices.

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

• Not applicable (see point 3).

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 or AI-assisted study is mentioned. This device is a fiberoptic light, not an AI diagnostic tool.

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

• Not applicable (see point 5).

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

• Not applicable. The "ground truth" for this device appears to be functional safety (electrical, EMC) and meeting its intended purpose as a light source, likely assessed through engineering tests and comparison to predicate devices, rather than a diagnostic performance ground truth.

8. The sample size for the training set

• Not applicable. This device is not an AI/machine learning model that would require a training set.

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

• Not applicable (see point 8).

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FlexScope is intended to be used for visualization of tissue in The general surgical applications.

The FlexScope™ is a flexible deflectable endoscope intended to allow physicians to visualize tissue in body cavities and through natural body orifices. The device is designed to be advanced to the site of interest through a cannula or trocar or may be used in an open procedure. The device will is available in various diameters.

The method of construction and materials used for these flexible fiber optic endoscopes are substantially equivalent to previous endoscope products.

This document describes a traditional medical device (an endoscope), not an AI/ML powered device. Therefore, the requested information regarding acceptance criteria, study details, ground truth, and AI/ML specific aspects (like multi-reader multi-case studies, standalone performance, training sets, etc.) is not applicable.

The provided text focuses on the physical characteristics, materials, and intended use of the FlexScope™ Fiber Optic Endoscope, comparing it to predicate devices to establish substantial equivalence.

Here's a breakdown of the relevant information from the provided text, recognizing the absence of AI/ML specific details:

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

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. This is a physical device, and the testing described is primarily engineering and material-based, not data-driven (like an AI/ML model). There's no "test set" in the context of data for an algorithm.

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, in the context of AI/ML, refers to labels or diagnoses. For a physical endoscope, "ground truth" relates to engineering specifications and performance standards which are met through physical testing.

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

Not applicable. This refers to adjudication of expert opinions for ground truth in AI/ML studies.

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 type of study is specifically for AI/ML algorithms and their impact on human reader performance. The FlexScope™ is a physical visualization tool.

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

Not applicable. This refers to the performance of an AI/ML algorithm by itself.

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

Not applicable in the AI/ML sense. The "ground truth" for the FlexScope™ would be objective engineering standards and observed physical performance during testing.

8. The sample size for the training set

Not applicable. There is no AI/ML model to train.

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

Not applicable. There is no AI/ML model to train.

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