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The Cellvizio® 100 Series System with Confocal Miniprobes™ is a confocal laser system with fiber optic probes that is intended to allow imaging of the internal microstructure of tissues in anatomical tracts, i.e., gastrointestinal or respiratory, accessed by an endoscope or endoscopic accessories.

Cellvizio® 100 Series is a confocal laser imaging system with a variety of fiber optic probes (Confocal Miniprobes™) that is intended to allow confocal laser imaging of the internal microstructure of tissues in anatomical tracts, i.e. gastrointestinal or respiratory, accessed through an endoscope. Cellvizio® 100 Series is based on a common laser scanning technology adapted for imaging through a bundle of optical fibers which is the raw component of the Confocal Miniprobes™. Cellvizio® 100 Series is composed of several components, including: Main opto-electronical components: Laser Scanning Unit (LSU) Confocal Miniprobes™ Confocal Processor with Cellvizio® Software Peripherals: Foot-switch Keyboard Trackball Screen Video converter lsolation transformer User documentation: Cellvizio® 100 Series System User Guide, Confocal Miniprobes™ User Guide and Reprocessing Instructions Accessories (such as Cletop-S Confocal Miniprobes™ connector cleaning system, Confocal Miniprobes™ clip, storage box, caps, späre fuses) All components are integrated into a cart.

The provided document describes a Special 510(k) submission for the Cellvizio® 100 Series, focusing on device modifications. This type of submission is used when changes do not alter the scientific premise, technological characteristics, or intended use of the device, implying that the established performance criteria from previous clearances (K051585 and K061666) are still valid. The submission primarily aims to demonstrate that the upgrades do not introduce new concerns regarding safety or effectiveness and do not affect image quality and imaging performance compared to the predicate devices.

Therefore, the document does not contain a specific study designed to prove the device meets new acceptance criteria for diagnostic performance, nor does it provide a table of acceptance criteria with reported device performance in terms of diagnostic metrics (e.g., sensitivity, specificity). Instead, the performance evaluation is focused on maintaining equivalence with predicate devices through bench testing.

Here's an attempt to answer the questions based on the available information, noting where specific details are not provided:

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

The document does not explicitly state quantitative acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy) or present a table comparing such criteria to reported device performance.
Instead, the performance evaluation is focused on demonstrating that "the upgrades made to the system do not introduce any new concerns related to the safety or effectiveness compared to the predicate devices" and that "both software and hardware updates do not affect image quality and imaging performance."

The implicit acceptance criteria are that the Cellvizio® 100 Series performs equivalently or better than the predicate devices (Cellvizio® F-400 System (K051585) and Cellvizio® (-GI, -LUNG) with Confocal Miniprobe™ (K061666)) in terms of safety, effectiveness, image quality, and imaging performance.

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

The document mentions "bench testing" using "representative tissue samples." It does not specify the sample size of these tissue samples. There is no information regarding the country of origin of the data or whether the testing was retrospective or prospective in a clinical setting.

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)

The document refers to "comparison of images obtained in bench testing." It does not specify if experts were used to establish a ground truth for these images, nor does it mention the number or qualifications of any potential experts. Given it's a Special 510(k) for device modifications, the focus is on technical equivalence rather than establishing new clinical diagnostic accuracy with expert-derived ground truth.

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

No details on an adjudication method are provided, as the testing described is technical bench testing rather than a clinical study requiring adjudicated interpretations.

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 is mentioned. The device described is an imaging system, not an AI-assisted diagnostic tool in the context of this submission. The product "Cellvizio" is a confocal laser endomicroscope used for direct visualization of tissue microstructure.

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

This question is not applicable. The Cellvizio® 100 Series is an imaging system that provides images for human interpretation, not a standalone diagnostic algorithm.

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

For the "representative tissue samples" used in bench testing, the ground truth for image quality and performance would likely be based on technical specifications and visual comparison by engineers or trained personnel, ensuring the images generated by the modified device are comparable to those from the predicate device. There is no indication of clinical ground truth (e.g., pathology) being established for these samples in this specific submission.

8. The sample size for the training set

This question is not applicable. The document describes an imaging system, not an AI/ML algorithm that requires a training set.

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

This question is not applicable, as no training set for an AI/ML algorithm is described.

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