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The Confocal Microprobe Imaging System can enter the human body cavity or surgical channel through an endoscope, allowing confocal laser imaging of the microstructure of tissues, including but not limited to the identification of cells, vessels and their organization or architecture.

The working principle of Confocal Microprobe Imaging System is based on probe-based confocal laser endomicroscopy technology (pCLE). The system combines confocal technology and fiber beam imaging technology. The fiber Optic Microprobe can enter the human cavity through the endoscopic working channel and contact the tissue cells through the object lens at the front end of the Fiber Optic Microprobe. The imaging principle of the device is as follows:

The laser scanning beam emitted by the laser in the Laser Scanning System forms a light source through the grating pinhole and is transmitted to the focal plane of the fluorescent labeled tissue cells through the Fiber Optic Microprobe. The fluorescent substance in the measured tissue cell emits fluorescence under the excitation of the laser. The fluorescence signal is collected by Fiber Optic Microprobe front end object lens and transmitted through the fiber beam microprobe to the detecting hole and then is transmitted to the photomultiplier tube (PMT) of the Photoelectric detector and then to the host for signal analysis and processing. Finally, the image is formed on the computer monitoring screen after software processing.

Light emitted at the top and bottom of the focal plane of the tested tissue produces a large diameter spot (much larger than that of the detecting hole) at the detecting hole, thus only a very small part of the light can be received by the detector through the detecting hole. Moreover, the larger the distance from the focal plane of the object lens, the larger the diffuse spot produced by the non-targeted tissue in the detecting hole and the lesser energy passes through the detecting hole (from 10% to 1%, slowly close to 0%), thus the weaker the unwanted signal is generated on the detector, and smaller the impact is caused by non-targeted tissue. Because confocal microscopy only images the focal plane of the target tissue, it effectively avoids the interference of diffracting light and scattered light, so that it has a higher resolution than ordinary microscopy and has been widely used in biology.

It appears that the provided FDA 510(k) Clearance Letter does not contain detailed information about a clinical study involving human readers or a specific "acceptance criteria" table with reported performance metrics for an AI component.

The document discusses the Confocal Microprobe Imaging System, which is a hardware device for imaging tissues. While it mentions "software" and "cybersecurity," these sections focus on general software validation and cybersecurity considerations, not the performance evaluation of an AI algorithm designed to interpret or analyze the images beyond the device's basic function.

The "Performance Testing" section states that "Performance Verification Test has been conducted in accordance with the internal performance requirements stated in the Performance Validation Scheme (HRD0003932 & HRD0004124)" and lists technical performance requirements like "Field of view, Horizontal resolution, Depth of Observation, Frame rate." These relate to the imaging system's hardware performance, not an AI's diagnostic accuracy.

Therefore,Based on the provided FDA 510(k) clearance letter, I cannot fulfill your request for detailed information regarding acceptance criteria for an AI component and the study that proves the device meets those criteria. The letter primarily addresses the clearance of a Confocal Microprobe Imaging System (hardware), focusing on its substantial equivalence to predicate devices based on technological characteristics, biocompatibility, reprocessing, electrical safety, and general software/cybersecurity validation.

There is no mention of an AI-specific component, its performance criteria, or any clinical studies (e.g., MRMC studies) pertaining to AI algorithm performance within this document. The "Performance Testing" section refers to the optical and functional performance of the imaging system itself, not the diagnostic performance of an AI that might interpret the images generated.

If such an AI component exists, its performance evaluation would typically be described in a separate section with specific metrics like sensitivity, specificity, or AUC, and details about the study design (test set, ground truth, expert adjudication, etc.). This information is absent in the provided text.

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