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The MOR System is indicated for the stimulation of healthy perineal muscles and nerves in adult males to improve or enhance sexual performance. It is not intended to be used in conjunction with therapy or treatment of medical diseases or medical conditions of any kind.

The MOR System is a drug-free wearable patch system with single-use electrode patches, a reusable pulse generator, and a smartphone app. Together, these components deliver user-defined electrical stimulation to the perineal region.

The provided text is a 510(k) premarket notification document for a medical device called the "MOR System," a non-implanted electrical stimulation device intended to improve or enhance male sexual performance.

This document describes the device, its intended use, and its similarities to a predicate device (vPATCH). It details non-clinical testing performed, including electrical safety, EMC testing, software verification and validation (per IEC 62304), and biocompatibility testing (per ISO 10993-1).

However, the document does not contain any information about acceptance criteria or a study proving the device meets those criteria, particularly in the context of AI/algorithm performance metrics like sensitivity, specificity, or AUC, or comparative effectiveness studies with human readers.

The questions in the prompt refer to criteria and studies typically associated with the evaluation of AI/ML-driven medical devices, especially those involving image analysis or diagnostic aid, which require quantitative performance metrics and rigorous validation against expert ground truth.

Since the provided text does not contain the information required to answer the prompt's specific questions about acceptance criteria, performance studies, AI training/test sets, or ground truth establishment, I must state that this information is not available in the provided document.

The document primarily focuses on demonstrating substantial equivalence to a predicate device through shared technological characteristics and non-clinical safety testing, rather than reporting on the performance of an AI/ML algorithm or its impact on human reader performance.

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