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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.

The subject device, "Cellvizio® 100 Series F700-v2 system with Confocal Miniprobes" operates in the same way as the predicate devices in order to provide confocal images of the internal microstructure of tissues in anatomical tracts. The only difference between the devices is the laser used for imaging at 785nm, along with the filters that have been adapted to this wavelength. The Cellvizio® 100 Series F700-v2 system is equipped with a laser emitting at 785nm whereas predicate devices are equipped witha laser emitting at 488nm or 660nm.

Here's a breakdown of the acceptance criteria and study information for the Cellvizio® 100 Series System with Confocal Miniprobes™, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document does not specify sample sizes for test sets in terms of patient data or image data. The performance data primarily relies on engineering and regulatory compliance testing.

The provenance of this data (e.g., country of origin, retrospective/prospective) is not explicitly stated beyond stating "Performance Data on which Substantial Equivalence is Based". Given the nature of the tests (laser safety, electrical safety, spectral sensitivity, software validation), these are typically conducted in a laboratory setting by the manufacturer or accredited testing facilities.

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

This information is not provided in the document. The study described focuses on technical and regulatory compliance rather than clinical accuracy adjudicated by experts.

4. Adjudication Method for the Test Set:

An adjudication method (e.g., 2+1, 3+1) is not applicable or described in this document as the evaluation is based on technical specifications and functional equivalence, not on clinical performance requiring expert consensus on a test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study was not done or reported. The submission focuses on demonstrating substantial equivalence to a predicate device based on technical characteristics and safety/performance testing, not on comparative clinical effectiveness with or without AI assistance. The document is for a device that provides images, and the "AI" aspect (if any) is not discussed in terms of improving human reading.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

This device is an imaging system (endoscope and accessories) that produces images for human interpretation. There is no mention of an "algorithm only" or standalone performance study as there isn't an explicit algorithm being independently evaluated separate from the human-in-the-loop use. The primary focus is the performance of the hardware (laser, filters, electrical components, software) to produce images.

7. The Type of Ground Truth Used:

The ground truth used for these tests is based on established engineering standards and regulatory requirements. For example:

• Laser Safety: Ground truth is defined by the limits and specifications in IEC 60825-1:2007 and 21 CFR Part 1040.
• Electrical Safety: Ground truth is defined by the requirements in IEC 60601-1 series.
• Spectral Sensitivity: Ground truth would be the expected or specified spectral response of the system.
• Software Validation: Ground truth is the software design specifications and expected functionality.
• Image Quality: The ground truth for "similar image performance" is qualitative comparison against the predicate device's known image output.

It is not based on expert consensus, pathology, or outcomes data in the clinical sense.

8. The Sample Size for the Training Set:

The document does not mention a training set. This type of submission (510(k) for a hardware modification) does not typically involve machine learning or AI models that require training sets in the computational sense. The "software validation" mentioned refers to traditional software engineering validation, not AI model training.

9. How the Ground Truth for the Training Set Was Established:

As there is no training set mentioned, this question is not applicable to the provided document.

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