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The MR system is an imaging device, and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross-sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

The QD C-Spine Coil is being added to increase the clinical utility of the MRP-7000 in the stationary and mobile configurations. The new coil is intended to provide high quality imaging of cervical spine anatomy; dependent upon patient anatomy, the coil sensitivity may additionally effectively image upper thoracic and lower cranial structures. The QD C-Spine utilizes a proprietary multiple receiver quadrature coil design for improved S/N Ratio.

The QD C-Spine Coil is being added to increase the clinical utility of the AIRIS in the stationary configuration. The new coil is intended to provide high quality imaging of cervical spine anatomy, dependent upon patient anatomy, the coil sensitivity may additionally effectively image upper thoracic and lower cranial structures. The QD C-Spine utilizes a proprietary multiple receiver quadrature coil design for improved S/ N Ratio.

The provided document is a 510(k) Summary of Safety and Effectiveness for the Hitachi QD C-Spine Coil for MRP-7000 and AIRIS MRI systems, dated March 13, 1997. It describes a medical device, but it is not an AI/ML powered device. The document does not contain information on acceptance criteria, device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details as it predates the widespread use of AI/ML in medical devices.

Therefore, I cannot extract the requested information regarding AI/ML device acceptance criteria and study details from this document.

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