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The Poseidon Ultrasound System is indicated for use by qualified and trained healthcare professionals to enable diagnostic ultrasound imaging and measurement of anatomical structures and fluids of adult and pediativ for the following clinical applications: Peripheral Vessel (including carotid and arterial studies), Procedural Guidance, Small Organs (including thyroid), Cardiac, Abdominal, Urology, Fetal/Obstetric, Gynecological, Musculoskeletal (conventional) and Musculoskeletal (superficial).

The Poseidon Ultrasound System is a general-purpose diagnostic imaging system for use by qualified and trained healthcare professionals enabling visualization and measurement of anatomical structures and fluid on adult and pediatric patients. The system consists of a single transducer with broad imaging capabilities connected via a USB cable to a standard handheld commercial off the shelf (COTS) mobile device. In addition to M-mode and B-mode imaging the instrument also supports Color Flow Doppler imaging.

The user interface includes touchscreen menus, buttons, controls, indicators and navigation icons that allow the operator to control the system and to view and measure ultrasound imagery.

The provided text is a 510(k) summary for the Butterfly Network Poseidon Ultrasound System. It describes the device, its intended use, and its equivalence to a predicate device. However, it does not contain information about acceptance criteria or a study that proves the device meets those criteria in the context of AI/ML performance.

This document focuses on the mechanical, electrical, and physical safety and performance of the ultrasound system itself, and its substantial equivalence to a previously cleared device (Philips Healthcare, Lumify Ultrasound System). It cites compliance with standards like IEC 60601-1, IEC 60601-1-2, IEC 60601-2-37, ISO 10993:2009, and NEMA UD-2.

There is no mention of an AI/ML component in the Poseidon Ultrasound System, nor any data or study design related to algorithmic performance as would be required for an AI/ML-driven medical device. Therefore, I cannot provide the requested information regarding acceptance criteria and a study proving an AI component meets those criteria.

The information you asked for (acceptance criteria for an AI component, sample sizes, expert qualifications, ground truth, etc.) is typically found in submissions for AI/ML-enabled devices, which this document does not appear to describe.

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