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The S-scan Temporomandibular Coil is to be used in MR imaging of the temporomandibular joint.

S-scan is a Magnetic Resonance (MR) system that produces transverse, sagittal and coronal and oblique cross-section images of the limbs, joints and spinal column. It is intended for imaging portions of the upper limb, including the hand, wrist, forearm, elbow, arm and shoulder, imaging portions of the lower limb, including the foot, ankle, calf, knee, thigh and hip, imaging of the temporomandibular joint and imaging the cervical spine and the lumbar spine sections as portions of the spinal column. S-scan images correspond to the spatial distribution of protons (hydrogen nuclei) that determine magnetic resonance properties and are dependent on the MR parameters, including spin-lattice relaxation time (TI), spin-spin relaxation time (T2), nuclei density, flow velocity and "chemical shift". When interpreted by a medical expert trained in the use of MR equipment, the images can provide diagnostically useful information.

S-scan is a Magnetic Resonance (MR) system, which produces images of the internal structures of the patient's limbs and joints.

This 510(k) is to add the Temporomandibular coil. This is a single channel surface receiving coil, shaped for suitability to the areas under examination and designed to be matched to the patient's temporomandibular joint; the coil is also morphologically adapted to the examined area to obtain a good Signal-to-Noise Ratio.

The provided text is a 510(k) summary for a medical device (a Temporomandibular coil for an S-scan MRI system) and does not describe a study with acceptance criteria and reported device performance in the way a diagnostic AI/ML device submission would. Instead, it focuses on demonstrating "substantial equivalence" of the new coil to existing, legally marketed predicate devices.

Therefore, many of the typical acceptance criteria and study details for an AI/ML device will not be found in this document. The "performance data" section explicitly states "Non-clinical testing of the S-scan system with the addition of the Temporomandibular Coil demonstrated that it met performance requirements and is as safe and effective as the predicate devices." This implies engineering and safety testing, not a clinical study to assess diagnostic accuracy or the impact of AI.

I will fill in the requested information as much as possible based on the provided text, and explicitly state when the information is not available due to the nature of this type of submission.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) for an MRI coil, the acceptance criteria are not in terms of diagnostic accuracy metrics (e.g., sensitivity, specificity) for an AI/ML algorithm. Instead, the "performance requirements" would typically relate to the coil's physical properties, image quality metrics (e.g., Signal-to-Noise Ratio), and safety (e.g., heating, electromagnetic compatibility) when integrated with the existing S-scan MRI system. The document does not provide specific numerical acceptance criteria or reported values for these engineering performance requirements.

The core "acceptance criterion" for a 510(k) submission is demonstrating substantial equivalence to a predicate device.

2. Sample size used for the test set and the data provenance

This information is not provided because the submission describes non-clinical testing for an MRI coil, not a clinical study involving patient data for an AI/ML algorithm. "Non-clinical testing" typically involves engineering bench tests and phantom studies.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. Ground truth, in the context of diagnostic accuracy, is not established for this type of submission. The device's "effectiveness" is tied to its ability to produce images that a "medical expert trained in the use of MR equipment" can interpret, rather than an algorithm making a diagnosis.

4. Adjudication method for the test set

Not applicable, as there is no clinical test set for diagnostic accuracy requiring ground truth adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No. This K110373 submission is for an MRI coil, not an AI/ML device, and therefore no MRMC study or AI assistance comparison was conducted or is relevant.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

No. This is an MRI coil, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. For this device, "performance" relates to the technical specifications and image quality necessary for a medical expert to make a diagnosis, not the diagnostic accuracy of an algorithm against a specific ground truth.

8. The sample size for the training set

Not applicable. This is for an MRI coil, not an AI/ML algorithm.

9. How the ground truth for the training set was established

Not applicable. This is for an MRI coil, not an AI/ML algorithm.

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