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The Point-of-Care Magnetic Resonance Imaging Device is a bedside magnetic resonance imaging device for producing images that display the internal structure of the head where full diagnostic examination is not clinically practical. When interpreted by a trained physician, these images provide information that can be useful in determining a diagnosis.

The Point-Of-Care Magnetic Resonance Imaging (POC MRI) system is an MRI device that is portable allowing patient bedside imaging. It enables visualization of the internal structures of the head using standard magnetic resonance imaging contrasts. The main interface is a commercial off-the-shelf device, used to operate the system, provide access to patient data, exam set-up, exam execution, and MRI image data viewing for quality control purposes as well as for cloud storage interactions. The POC MRI system can generate MRI data sets with a broad range of contrasts.

The user interface includes touch screen menus, controls, indicators and navigation icons that allow the operator to control the system and to view imagery.

Based on the provided text, the device in question is the Hyperfine Point-Of-Care Magnetic Resonance Imaging Scanner System.

Here's an analysis of the acceptance criteria and the study information, extracting what's available in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a specific table of quantitative acceptance criteria for image quality or diagnostic performance, nor does it report specific performance metrics for the device against such criteria. Instead, it refers to verification and validation activities conducted to demonstrate that the device meets "predetermined performance specifications" and several recognized standards.

2. Sample Size Used for the Test Set and Data Provenance

The document does not provide any details regarding the sample size used for a test set, nor does it specify the data provenance (e.g., country of origin, retrospective/prospective) for any studies involving human subjects or patient data. The evaluation appears to be focused on technical standards and phantom testing as opposed to clinical study results presented in this 510(k) summary.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

Given the lack of information on a clinical test set with human subjects, the document does not specify the number of experts used to establish ground truth or their qualifications. The "Indications for Use" section mentions that images, "When interpreted by a trained physician, these images provide information that can be useful in determining a diagnosis." However, this is a general statement about usage, not a description of a ground truth establishment process for a study.

4. Adjudication Method for the Test Set

As no test set with human subjects or expert assessment is described, there is no information on any adjudication method.

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it mention any AI assistance or the effect size of such assistance on human readers. The summary focuses on the MRI system itself and its equivalence to a predicate device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The document does not describe any standalone algorithm performance testing. The device is a diagnostic imaging system, and its output is intended for interpretation by a trained physician.

7. The Type of Ground Truth Used

The document does not specify the type of ground truth used for any clinical performance evaluation, as no such evaluation is detailed in this summary. The standards listed (e.g., NEMA standards for SNR, uniformity, geometric distortion) relate to technical performance characteristics typically evaluated using phantoms and standardized testing procedures, not clinical ground truth.

8. The Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. This suggests the 510(k) summary does not involve a machine learning model that would require a distinct training set for diagnostic classification in the typical sense.

9. How the Ground Truth for the Training Set Was Established

Since no training set is mentioned, there is no information on how ground truth for a training set was established.

Summary of Information from the Document:

The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (K192002 - Lucy Point-of-Care Magnetic Resonance Imaging Device, Hyperfine Research, Inc.) based on technological characteristics, safety, and performance testing against recognized standards. It does not contain details about specific clinical performance studies involving patient data, expert interpretations, or AI-driven diagnostic capabilities. The performance evaluation primarily refers to adherence to engineering and safety standards using in-house protocols.

Standards cited for evaluating performance:

• ANSI AAMI 60601-1: 2005 (+ Amendment 1) Medical Electrical Equipment -Part 1: General Requirements for Safety
• IEC 60601-2-33: Edition 3.2 - 2015. Medical Electrical Equipment - Part 2-33: Particular Requirements for the Basic Safety and Essential Performance of Magnetic Resonance Equipment for Medical Diagnosis
• IEC 60601-1-2: Edition 4.0 - 2014, Medical Electrical Equipment- Part 1-2: General Requirements for Safety - Collateral Standard: Electromagnetic Compatibility - Requirements and Tests
• IEC 60601-1-6: Edition 3.1 - 2013. Medical Electrical Equipment - Part 1-6: General Requirements for Basic Safety and Essential Performance -Collateral Standard: Usability
• ISO 10993-1: Edition 5 - 2018 Biological Evaluation of Medical Devices, Part 1
• ISO 14971: 2007 (R)2010 Application of Risk Management to Medical Devices
• IEC 62304: 2006 (+ Amendment 1) Medical Device Software - Software Lifecycle Process
• NEMA MS 1-2008 (R2014) Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Imaging
• NEMA MS 3-2008 (R2014) Determination of Image Uniformity in Diagnostic Magnetic Resonance Images
• NEMA MS 8-2008 Characterization of the Specific Absorption Rate for Magnetic Resonance Imaging Systems
• NEMA MS 9-2008 (R2014) Characterization of Phased Array Coils for Diagnostic Magnetic Resonance Images
• NEMA MS 12-2016 Quantification and Mapping of Geometric Distortion for Special Applications

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