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The endoscope is a sterile, single endoscope intended for endoscopic procedures and examination within the nasal lumens and upper airway anatomy. The endoscope is intended to provide visualization via an Image Processor. The endoscope is intended for use in a hospital environment. It is designed for use in adults.

The Rhinolaryngoscope System consists of Single-Use Rhinolaryngoscope (six models shown in below) to be introduced within the nasal lumens and upper airway anatomy and Image Processor (model: VLM-02) for clinical image processing. The Image Processor provides power and processes the images for medical electronic endoscope. The Single-Use Rhinolaryngoscope is a sterile single used flexible Rhinolaryngoscope. The Image Processor is a reusable monitor.

The document provided is a 510(k) premarket notification for a medical device, the Rhinolaryngoscope System. It details the device's characteristics, intended use, and comparison to predicate devices, along with performance data. However, this document does not contain information about an AI/ML-based device or a comparative effectiveness study involving human readers and AI assistance.

The device described is a Rhinolaryngoscope system, which is an endoscope for visualization. The performance data provided are primarily related to the physical and electrical aspects of the device, such as biocompatibility, sterilization, electrical safety, EMC, and bench performance (optical performance, mechanical bending, photobiological safety, thermal safety). There is no mention of an algorithm or AI component that would require an acceptance criteria table for AI performance metrics, a test set with ground truth from experts, or multi-reader multi-case studies.

Therefore, I cannot fulfill the request for information on acceptance criteria and study proving an AI/ML device meets them based on the provided text. The questions regarding sample size, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone algorithm performance, type of ground truth, training set size, and how training set ground truth was established are irrelevant to the information present in this 510(k) submission, as it does not pertain to an AI/ML component.

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