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The GE 1.5T 8 Channel Torso Coil is intended to be used in the abdomen, pelvis and chest regions for 2D and 3D Magnetic Resonance imaging and Parallel body imaging.

The GE 1.5T 8 Channel Torso Coil is a 12 element coil with integrated preamplifiers that provides optimized geometry for parallel imaging. The coil is designed to provide imaging of the abdomen, pelvis and chest regions. The device utilizes technology similar to the GE 8 Channel Cardiac Phased Array Coil (K022669), but is designed to image the torso, similar to the GE 3.0T Torso Phased Array Coil (K030495).

The provided text describes a 510(k) summary for a medical device, specifically the GE 1.5T 8 Channel Torso Coil. However, it does not contain the detailed information required to answer your prompt about acceptance criteria and a study proving the device meets those criteria for software or AI-driven systems.

This document is for a magnetic resonance coil, which is a hardware component, not a software or AI device. The "Summary of Studies" section only states: "Testing was performed to demonstrate that the design of the 1.5T 8 Channel Torso Coil meet predetermined acceptance criteria." It does not provide any specifics about:

• The acceptance criteria themselves.
• The reported device performance against those criteria.
• Sample sizes, data provenance, ground truth establishment, expert qualifications, adjudication methods, or MRMC/standalone study details.

These types of details are typically found in regulatory submissions for AI/ML-driven devices, which involve performance metrics like sensitivity, specificity, AUC, and their validation against clinical ground truth.

Therefore, based on the provided text, I cannot complete the table or answer the specific questions posed, as the information is not present for this type of device submission.

Below is a general template of how such an answer would be structured if the necessary information were available in the document:

1. Table of Acceptance Criteria and the Reported Device Performance:

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

• Test Set Sample Size: [Number of cases/patients]
• Data Provenance: [e.g., Retrospective or Prospective, Country(ies) of origin, e.g., "Multi-site retrospective study across 3 hospitals in the US and Germany."]

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

• Number of Experts: [e.g., 3]
• Qualifications of Experts: [e.g., "Board-certified radiologists, each with 10+ years of experience in abdominal imaging."]

4. Adjudication method for the test set:

• [e.g., "2+1 adjudication method: Initial consensus by two experts; in cases of disagreement, a third senior expert provided a final decision."]

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:

• [e.g., "Yes, a prospective MRMC study was conducted. Readers demonstrated a significant improvement in diagnostic accuracy (e.g., 15% increase in AUC) when using the AI-assisted workflow compared to reading without AI assistance."] (If not done, state "Not explicitly mentioned" or "No.")

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

• [e.g., "Yes, a standalone performance evaluation was conducted using the test set described above."] (If not done, state "Not explicitly mentioned" or "No.")

7. The type of ground truth used:

• [e.g., "Histopathological confirmation (biopsy results) as the gold standard." or "Expert consensus derived from a panel of radiologists reviewing all available clinical and imaging data." or "Long-term patient outcomes data."]

8. The sample size for the training set:

• [Number of cases/patients]

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

• [e.g., "Similar to the test set, ground truth for the training set was established via expert consensus by a separate panel of board-certified radiologists." or "Leveraged existing annotated datasets from public repositories combined with internal expert labeling."]

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